
import pandas as pd
import numpy as np
import math

# Visualisierung
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(context='notebook', style='whitegrid', palette='muted')


# import MLs
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier

from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier

from sklearn.model_selection import GridSearchCV


# import metrics
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score


# Datavorbereitung & -transformation
from sklearn.model_selection import train_test_split
from sklearn.compose import make_column_selector
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import make_pipeline


data = pd.read_csv("train.csv", sep=',', engine='python')
data.head()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8693 entries, 0 to 8692
Data columns (total 14 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   PassengerId   8693 non-null   object 
 1   HomePlanet    8492 non-null   object 
 2   CryoSleep     8476 non-null   object 
 3   Cabin         8494 non-null   object 
 4   Destination   8511 non-null   object 
 5   Age           8514 non-null   float64
 6   VIP           8490 non-null   object 
 7   RoomService   8512 non-null   float64
 8   FoodCourt     8510 non-null   float64
 9   ShoppingMall  8485 non-null   float64
 10  Spa           8510 non-null   float64
 11  VRDeck        8505 non-null   float64
 12  Name          8493 non-null   object 
 13  Transported   8693 non-null   bool   
dtypes: bool(1), float64(6), object(7)
memory usage: 891.5+ KB


# Den Prozentsatz der leeren Werte für jede Spalte
data.isnull().sum()

d = {
    'Column': data.isnull().sum().index,
    'Num Nulls': data.isnull().sum().values,
    '% Nulls': ((data.isnull().sum().values / data.shape[0]) * 100).round(2)}
nulls_df = pd.DataFrame(data=d)

nulls_df


##  Verteilung der unbekannten Werte in Abhängigkeit vom Wert der Zielvariablen

columns=[]
transported=[]
not_transported=[]

for col in data.columns:
    if data[col].isnull().sum() > 0:
        tmp = data.loc[data[col].isnull(), ['Transported']].groupby('Transported').size()
        columns.append(col)
        transported.append(round(tmp[True] / tmp.values.sum() * 100, 2))
        not_transported.append(round(tmp[False] / tmp.values.sum() * 100, 2))

d = {'Columns' : columns, 'transported %' : transported, 'not_transported %': not_transported }   

null_distrib_df = pd.DataFrame(data=d)

null_distrib_df


# nulls in Zeilen
tmp = data.isnull().sum(axis=1)
nulls_in_rows = pd.DataFrame(data = {'row' : tmp.index, 'null_num' : tmp.values})
nulls_in_rows.groupby('null_num', as_index=False).size()


# Zeile mit nulls anschauen
data.iloc[data.isnull().sum(axis=1).gt(2).values, :]


# den Inhalt der Object Spalten anzeigen 

category_features = data.select_dtypes(include="object").columns

for c in category_features:
    if c != 'name':
        print(f"Column: '{c}'",)
        print(data.loc[:, c].unique())

Column: 'PassengerId'
['0001_01' '0002_01' '0003_01' ... '9279_01' '9280_01' '9280_02']
Column: 'HomePlanet'
['Europa' 'Earth' 'Mars' nan]
Column: 'CryoSleep'
[False True nan]
Column: 'Cabin'
['B/0/P' 'F/0/S' 'A/0/S' ... 'G/1499/S' 'G/1500/S' 'E/608/S']
Column: 'Destination'
['TRAPPIST-1e' 'PSO J318.5-22' '55 Cancri e' nan]
Column: 'VIP'
[False True nan]
Column: 'Name'
['Maham Ofracculy' 'Juanna Vines' 'Altark Susent' ... 'Fayey Connon'
 'Celeon Hontichre' 'Propsh Hontichre']


for_visual_df = data.copy()


for_visual_df[['CabinDeck', 'CabinNum', 'CabinSide']] = for_visual_df['Cabin'].str.split('/', expand=True)


for_visual_df[['GroupId', 'NumInGroup']] = for_visual_df['PassengerId'].str.split('_', expand=True)


# numerische Spalten in den Typ float umwandeln
for_visual_df['CabinNum'] = for_visual_df.loc[:,'CabinNum'].astype(float) 
for_visual_df['GroupId'] = for_visual_df.loc[:,'GroupId'].astype(float)


for_visual_df[['FirstName', 'LastName']] = for_visual_df['Name'].str.split(' ', expand=True)


GroupSize_df = for_visual_df.groupby('GroupId', as_index=False).agg(
                                    GroupSize = ('PassengerId', 'count'))
    

for_visual_df = for_visual_df.merge(GroupSize_df, how='left', left_on='GroupId', right_on='GroupId')
for_visual_df.head(5)


for_visual_df['TotalSpend'] = for_visual_df.loc[:,
                        ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']].sum(axis=1, min_count=5)


for_visual_df['Route'] = for_visual_df['HomePlanet'] + ' - ' +for_visual_df['Destination']


# die Anzahl der Kabinen in jeder Gruppe
for_visual_df.groupby(['GroupId'],as_index=False).agg(cabins=('CabinNum', lambda x: len(x.unique()))).sort_values('cabins', ascending=False)


for_visual_df['IsSingle'] = for_visual_df.loc[:, 'GroupSize'].apply(lambda x: x==1 )


for_visual_df['NoSpend'] = for_visual_df.loc[:, 'TotalSpend'].apply(lambda x: np.nan if math.isnan(x) else x==0)


for_visual_df['IsChild'] = for_visual_df.loc[:, 'Age'].apply(lambda x: np.nan if math.isnan(x) else x < 18)


namesakes_in_group_df = for_visual_df.groupby(['LastName', 'GroupId'], as_index=False).agg(
                namesakes_num_in_group = ('PassengerId', 'count'))

for_visual_df = for_visual_df.merge(
    namesakes_in_group_df, how='left',
    left_on=['LastName', 'GroupId'],
    right_on=['LastName', 'GroupId'])

for_visual_df['namesakes_num_in_group'] = for_visual_df['namesakes_num_in_group'] - 1


# #2. число разных фамилий в группе
# namesakes_in_group_df = for_visual_df.groupby(['GroupId'], as_index=False).agg(
#                 namesakes_num_in_group = ('LastName', lambda x: len(x.unique())))

# for_visual_df = for_visual_df.merge(namesakes_in_group_df,
#                                     how='left',
#                                     left_on=['LastName', 'GroupId'],
#                                     right_on=['LastName', 'GroupId'])

# for_visual_df['namesakes_num_in_group'] = for_visual_df['namesakes_num_in_group'] - 1


# #3. число кабин с однофамильцами
# cabins_with_namesakes_df = namesakes_in_cabin_df.groupby(['LastName'], as_index=False).agg(
#                     cabins_num_with_namesakes = ('CabinNum', 'count'))

# for_visual_df = for_visual_df.merge(cabins_with_namesakes_df, how='left', left_on='LastName', right_on='LastName')
# for_visual_df['cabins_num_with_namesakes'] = for_visual_df['cabins_num_with_namesakes'] - 1


for_visual_df['NameLength'] = for_visual_df.loc[:, 'Name'].str.len()


for_visual_df.head(10)


fig, axs = plt.subplots(figsize=(10, 8))
d = for_visual_df.groupby('Transported', as_index=False).size()

axs.pie(d['size'], labels= d['Transported'], autopct='%1.1f%%')

([<matplotlib.patches.Wedge at 0x1bc25bc1f70>,
  <matplotlib.patches.Wedge at 0x1bc25bcc820>],
 [Text(0.012522008688130899, 1.0999287246446539, 'False'),
  Text(-0.012522008688131034, -1.0999287246446539, 'True')],
 [Text(0.006830186557162308, 0.5999611225334475, '49.6%'),
  Text(-0.006830186557162382, -0.5999611225334475, '50.4%')])


for_visual_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8693 entries, 0 to 8692
Data columns (total 29 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   PassengerId             8693 non-null   object 
 1   HomePlanet              8492 non-null   object 
 2   CryoSleep               8476 non-null   object 
 3   Cabin                   8494 non-null   object 
 4   Destination             8511 non-null   object 
 5   Age                     8514 non-null   float64
 6   VIP                     8490 non-null   object 
 7   RoomService             8512 non-null   float64
 8   FoodCourt               8510 non-null   float64
 9   ShoppingMall            8485 non-null   float64
 10  Spa                     8510 non-null   float64
 11  VRDeck                  8505 non-null   float64
 12  Name                    8493 non-null   object 
 13  Transported             8693 non-null   bool   
 14  CabinDeck               8494 non-null   object 
 15  CabinNum                8494 non-null   float64
 16  CabinSide               8494 non-null   object 
 17  GroupId                 8693 non-null   float64
 18  NumInGroup              8693 non-null   object 
 19  FirstName               8493 non-null   object 
 20  LastName                8493 non-null   object 
 21  GroupSize               8693 non-null   int64  
 22  TotalSpend              7785 non-null   float64
 23  Route                   8314 non-null   object 
 24  IsSingle                8693 non-null   bool   
 25  NoSpend                 7785 non-null   object 
 26  IsChild                 8514 non-null   object 
 27  namesakes_num_in_group  8493 non-null   float64
 28  NameLength              8493 non-null   float64
dtypes: bool(2), float64(11), int64(1), object(15)
memory usage: 1.9+ MB


category_features = [
    'HomePlanet', 'Destination', 'CryoSleep', 'VIP', 'CabinDeck', 'CabinSide', 'IsChild', 'NoSpend', 'IsSingle']

fig, axs = plt.subplots(9, 2, figsize=(20, 55))
i = 0
axe = axs.ravel()

for f in category_features: #i, f in enumerate(category_features):
    d1 = for_visual_df.groupby(f, as_index=False, dropna=False).size()
    d1= d1.fillna('Unknown')
    axe[i].pie(d1['size'], labels=d1[f], autopct='%1.1f%%')
    axe[i].set_title(f)
    i+=1
    
    d2 = for_visual_df.groupby([f, 'Transported'], as_index=False, dropna=False).size()
    d2= d2.fillna('Unknown')
    sns.barplot(x=f, y="size",
                hue="Transported",
                data=d2, ax=axe[i])
    i+=1


fig, axs = plt.subplots(figsize=(10, 5))

d = for_visual_df.groupby(['Route', 'Transported'], as_index=False, dropna=False).size()
d= d.fillna('Unknown')

sns.barplot(x='size', y="Route",
                hue="Transported",
                data=d)

<AxesSubplot:xlabel='size', ylabel='Route'>


sns.histplot(data=for_visual_df, x='RoomService', bins=40)

<AxesSubplot:xlabel='RoomService', ylabel='Count'>


num_features = ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend' ]

for f in num_features:
    for_visual_df[f+'_log'] = np.log10(for_visual_df[f] + 1)


for_visual_df.head(5)


fig, axs = plt.subplots(figsize=(15, 7))
sns.histplot(data=for_visual_df, x='Age', bins=80, hue="Transported", kde=True)

<AxesSubplot:xlabel='Age', ylabel='Count'>


num_features = ['RoomService', 'RoomService_log',
                'FoodCourt', 'FoodCourt_log',
                'ShoppingMall','ShoppingMall_log',
                'Spa', 'Spa_log',
                'VRDeck', 'VRDeck_log']

fig, axs = plt.subplots(5, 2, figsize=(20, 30))
axe = axs.ravel()

for i, f in enumerate(num_features):
    sns.histplot(data=for_visual_df, x=f, ax=axe[i], bins=40, hue="Transported", kde=True)
    if i%2:
#        axe[i].set(xscale="log")
        axe[i].set_xlim(0,4.5)
        axe[i].set_ylim(0,220)


fig, axs = plt.subplots(1, 2, figsize=(20, 7))
axe = axs.ravel()

sns.histplot(data=for_visual_df, x='TotalSpend', ax=axe[0], bins=40, hue="Transported", kde=True)
sns.histplot(data=for_visual_df, x='TotalSpend_log', ax=axe[1], bins=40, hue="Transported", kde=True)

<AxesSubplot:xlabel='TotalSpend_log', ylabel='Count'>


fig, axs = plt.subplots(figsize=(10, 5))
d = for_visual_df.groupby(['GroupSize', 'Transported'], as_index=False, dropna=False).size()

sns.barplot(x='GroupSize', y="size",
                hue="Transported",
                data=d)

<AxesSubplot:xlabel='GroupSize', ylabel='size'>


d=for_visual_df.groupby(['Destination', 'HomePlanet']).size().unstack().fillna(0)

fig, axs = plt.subplots(figsize=(12, 6))
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r')

<AxesSubplot:xlabel='HomePlanet', ylabel='Destination'>


d=for_visual_df.groupby(['HomePlanet', 'CabinDeck']).size().unstack().fillna(0)

fig, axs = plt.subplots(figsize=(12, 6))
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r')

<AxesSubplot:xlabel='CabinDeck', ylabel='HomePlanet'>


d=for_visual_df.groupby(['Destination', 'CabinDeck']).size().unstack().fillna(0)

fig, axs = plt.subplots(figsize=(12, 6))
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r')

<AxesSubplot:xlabel='CabinDeck', ylabel='Destination'>


d=for_visual_df.groupby(['Route', 'CabinDeck']).size().unstack().fillna(0)

fig, axs = plt.subplots(figsize=(12, 6))
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r')

<AxesSubplot:xlabel='CabinDeck', ylabel='Route'>


for_visual_df.loc[:, ['GroupId', 'Age', 'RoomService', 'FoodCourt', 'ShoppingMall',
                                   'Spa', 'VRDeck', 'CabinNum', 'GroupSize', 'TotalSpend']].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8693 entries, 0 to 8692
Data columns (total 10 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   GroupId       8693 non-null   float64
 1   Age           8514 non-null   float64
 2   RoomService   8512 non-null   float64
 3   FoodCourt     8510 non-null   float64
 4   ShoppingMall  8485 non-null   float64
 5   Spa           8510 non-null   float64
 6   VRDeck        8505 non-null   float64
 7   CabinNum      8494 non-null   float64
 8   GroupSize     8693 non-null   int64  
 9   TotalSpend    7785 non-null   float64
dtypes: float64(9), int64(1)
memory usage: 747.1 KB


sns.pairplot(for_visual_df.loc[:, ['GroupId', 'Age', 'RoomService_log', 'FoodCourt_log', 'ShoppingMall_log',
                                   'Spa_log', 'VRDeck_log', 'CabinNum', 'GroupSize', 'TotalSpend']],
             corner=True)

<seaborn.axisgrid.PairGrid at 0x1bc26f730a0>


fig, axs = plt.subplots(figsize=(12, 6))

sns.scatterplot(data=for_visual_df, x='CabinNum', y='GroupId', hue='CabinDeck', ax=axs)

<AxesSubplot:xlabel='CabinNum', ylabel='GroupId'>


fig, axs = plt.subplots(figsize=(12, 6))

sns.scatterplot(data=for_visual_df,x="Age", y="TotalSpend", hue='CabinDeck',ax=axs)

<AxesSubplot:xlabel='Age', ylabel='TotalSpend'>


for_visual_df.loc[for_visual_df['Age'] < 13, ['CabinDeck']].value_counts()

CabinDeck
G            548
F            173
E             23
C             16
B             15
A             12
D              2
dtype: int64


fig, axs = plt.subplots(3, 2, figsize=(20, 20))
axs = axs.ravel()
d=for_visual_df.groupby(['CryoSleep', 'HomePlanet']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[0])

d=for_visual_df.groupby(['CryoSleep', 'Destination']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[1])

d=for_visual_df.groupby(['CryoSleep', 'CabinDeck']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[2])

d=for_visual_df.groupby(['CryoSleep', 'CabinSide']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[3])

d=for_visual_df.groupby(['CryoSleep', 'IsChild']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[4])

d=for_visual_df.groupby(['CryoSleep', 'NoSpend']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[5])

# d=for_visual_df.loc[for_visual_df['IsChild']==False, :].groupby(['CryoSleep', 'NoSpend']).size().unstack().fillna(0)
# sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[6])

<AxesSubplot:xlabel='NoSpend', ylabel='CryoSleep'>


for_visual_df.groupby(['CryoSleep']).agg(
    max_spend=('TotalSpend', 'max'),
    min_spend=('TotalSpend', 'min'),
    count=('PassengerId', 'count'),
    min_age=('Age', 'min'),
    max_age=('Age', 'max')
)


d= for_visual_df.groupby(['CabinNum', 'CabinDeck'], as_index=False).agg(TotalSpendProCabin = ('TotalSpend', 'sum'))

fig, axs = plt.subplots(figsize=(12, 6))

sns.scatterplot(data=d, x='CabinNum', y='TotalSpendProCabin', ax=axs)

<AxesSubplot:xlabel='CabinNum', ylabel='TotalSpendProCabin'>


fig, axs = plt.subplots(3, 1, figsize=(15, 13))

d=for_visual_df.groupby(['VIP', 'HomePlanet']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[0])

d=for_visual_df.groupby(['VIP', 'Destination']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[1])

d=for_visual_df.groupby(['VIP', 'CabinDeck']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs[2])

<AxesSubplot:xlabel='CabinDeck', ylabel='VIP'>


for_visual_df.loc[(for_visual_df['VIP'].notnull()) & (for_visual_df['VIP']) & (for_visual_df['HomePlanet'].isnull()), :]


fig, axs = plt.subplots(figsize=(10, 4))

d=for_visual_df.groupby(['CryoSleep', 'VIP']).size().unstack().fillna(0)
sns.heatmap(d, annot=True, fmt='g', cmap='mako_r', ax=axs)

<AxesSubplot:xlabel='VIP', ylabel='CryoSleep'>


# d = for_visual_df.groupby('GroupId', as_index=False).agg(
#     sum_cryosleep=('CryoSleep', 'sum'),
#     count = ('PassengerId','size'))
# d.sort_values('sum_cryosleep', ascending=False).head(5)


for_visual_df.columns

Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',
       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',
       'Name', 'Transported', 'CabinDeck', 'CabinNum', 'CabinSide', 'GroupId',
       'NumInGroup', 'FirstName', 'LastName', 'GroupSize', 'TotalSpend',
       'Route', 'IsSingle', 'NoSpend', 'IsChild', 'namesakes_num_in_group',
       'NameLength', 'RoomService_log', 'FoodCourt_log', 'ShoppingMall_log',
       'Spa_log', 'VRDeck_log', 'TotalSpend_log'],
      dtype='object')


name_features = ['namesakes_num_in_group', 'NameLength', ]

fig, axs = plt.subplots(2, 1, figsize=(10, 10))
i = 0
axe = axs.ravel()

for i, f in enumerate(name_features):
    d2 = for_visual_df.groupby([f, 'Transported'], as_index=False, dropna=False).size()
    d2= d2.fillna('Unknown')
    sns.barplot(x=f, y="size",
                hue="Transported",
                data=d2, ax=axe[i])


expenses_cols = ["RoomService", "FoodCourt", "ShoppingMall", "Spa", 'VRDeck']


data['Transported'] = data["Transported"].astype(int)


# trenne in trainings und test daten
data_train, data_test = train_test_split(data, train_size=0.8, random_state=7)
# data_train = data

# trenne in X_train/y_train und X_test/y_test 
X_train = data_train.drop(columns="Transported")
y_train = data_train.loc[:, "Transported"]


X_test = data_test.drop(columns="Transported")
y_test = data_test.loc[:, "Transported"]


# Data Transformationen:
# - Name - split: 'FirstName', 'LastName'
#- PassengerId - split: 'GroupId', 'NumInGroup' -- to float


def new_features_create(df):
    df[['FirstName', 'LastName']] = df['Name'].str.split(' ', expand=True)
    df[['GroupId', 'NumInGroup']] = df['PassengerId'].str.split('_', expand=True)
    df[['CabinDeck', 'CabinNum', 'CabinSide']] = df['Cabin'].str.split('/', expand=True)
    df['IsChild'] = df.loc[:, 'Age'].apply(lambda x: np.nan if math.isnan(x) else x < 18) 
    df['TotalSpend'] = df.loc[:, expenses_cols].sum(axis=1, min_count=5)
    
    df['GroupId'] = df.loc[:,'GroupId'].astype(float) 
    df['NumInGroup'] = df.loc[:,'NumInGroup'].astype(float)
    df['CabinNum'] = df.loc[:,'CabinNum'].astype(float)
    
    GroupSize_df = df.groupby('GroupId', as_index=False).agg(GroupSize = ('PassengerId', 'count'))
    df = df.merge(GroupSize_df, how='left', left_on='GroupId', right_on='GroupId', copy=False)

    df['IsSingle'] = df.loc[:, 'GroupSize'].apply(lambda x: x==1 )
    
    return df

    
X_train = new_features_create(X_train)


X_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6954 entries, 0 to 6953
Data columns (total 24 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   PassengerId   6954 non-null   object 
 1   HomePlanet    6788 non-null   object 
 2   CryoSleep     6789 non-null   object 
 3   Cabin         6797 non-null   object 
 4   Destination   6801 non-null   object 
 5   Age           6800 non-null   float64
 6   VIP           6788 non-null   object 
 7   RoomService   6808 non-null   float64
 8   FoodCourt     6806 non-null   float64
 9   ShoppingMall  6796 non-null   float64
 10  Spa           6802 non-null   float64
 11  VRDeck        6803 non-null   float64
 12  Name          6789 non-null   object 
 13  FirstName     6789 non-null   object 
 14  LastName      6789 non-null   object 
 15  GroupId       6954 non-null   float64
 16  NumInGroup    6954 non-null   float64
 17  CabinDeck     6797 non-null   object 
 18  CabinNum      6797 non-null   float64
 19  CabinSide     6797 non-null   object 
 20  IsChild       6800 non-null   object 
 21  TotalSpend    6223 non-null   float64
 22  GroupSize     6954 non-null   int64  
 23  IsSingle      6954 non-null   bool   
dtypes: bool(1), float64(10), int64(1), object(12)
memory usage: 1.3+ MB


X_train.head()


for_visual_df.loc[for_visual_df['HomePlanet'].notnull(), :].groupby(['GroupId'], as_index=False).agg(
    HomePlanet_num = ('HomePlanet', lambda x: len(x.unique()))).sort_values('HomePlanet_num', ascending = False)


for_visual_df.loc[
            for_visual_df['HomePlanet'].notnull(), :].groupby(['LastName'], as_index=False).agg(
                                num_planets=('HomePlanet', lambda x: len(x.unique()))).sort_values('num_planets', ascending = False)


def HomePlanet_update(df, source_df):
    print('------ HomePlanet_update ------------')
        
    # Menschen aus derselben Gruppe fliegen immer vom selben 'HomePlanet'
    planet_for_group_df = source_df.loc[source_df['HomePlanet'].notnull(), :].groupby(['GroupId'], as_index=False).agg(
                num_planets=('HomePlanet', lambda x: len(x.unique())),
                not_null_planet = ('HomePlanet', lambda x: x.unique()[0]))
    
    start_nulls_num = df.loc[df['HomePlanet'].isnull(), :].shape[0]
 
    print('HomePlanet NULLs:', start_nulls_num)
    
    
    df['HomePlanet'] = df.apply(
    lambda x: (planet_for_group_df.loc[planet_for_group_df['GroupId'] == x.GroupId, 'not_null_planet'].iloc[0] if planet_for_group_df.loc[planet_for_group_df['GroupId'] == x.GroupId, 'not_null_planet'].shape[0] > 0 else np.nan)
    if x.HomePlanet is np.nan else x.HomePlanet , axis=1)


    print('1. (after replacement through the GroupId) HomePlanet NULLs:', df.loc[df['HomePlanet'].isnull(), :].shape[0])
    
    #----------------------------------------------------------------------
    # wenn Deck A, B, C; T, 'Europa'. Wenn G, dann 'Earth'
    df.loc[(df['HomePlanet'].isnull()) & (df['CabinDeck'].isin(['A', 'B', 'C', 'T'])), 'HomePlanet']='Europa'
    df.loc[(df['HomePlanet'].isnull()) & (df['CabinDeck'].isin(['G'])), 'HomePlanet']='Earth'
    
    print('2. (after replacement through the Deck) HomePlanet NULLs:', df.loc[df['HomePlanet'].isnull(), :].shape[0])
    
    # ---------------------------------------------------------------------
    # Passagiere mit demselben 'LatName' fliegen von demselben Planeten
    planet_for_lastname_df = source_df.loc[
            source_df['HomePlanet'].notnull(), :].groupby(['LastName'], as_index=False).agg(
                                num_planets=('HomePlanet', lambda x: len(x.unique())),
                                not_null_planet = ('HomePlanet', lambda x: x.unique()[0]))
    
    df['HomePlanet'] = df.apply(
    lambda x: (planet_for_lastname_df.loc[planet_for_lastname_df['LastName'] == x.LastName, 'not_null_planet'].iloc[0] if planet_for_lastname_df.loc[planet_for_lastname_df['LastName'] == x.LastName, 'not_null_planet'].shape[0] > 0 else np.nan)
    if x.HomePlanet is np.nan else x.HomePlanet , axis=1)
    
    print('3. (after replacement through the LastName) HomePlanet NULLs:', df.loc[df['HomePlanet'].isnull(), :].shape[0])

    #----------------------------------------------------------------------
    df.loc[(df['HomePlanet'].isnull()) & (df['Destination'].isin(['TRAPPIST-1e', 'PSO J318.5-22'])) & (df['CabinDeck'] == 'D'), 'HomePlanet']='Mars'   
    df.loc[(df['HomePlanet'].isnull()) & ~(df['CabinDeck'] == 'D'), 'HomePlanet']='Earth'
    df.loc[(df['HomePlanet'].isnull()) & (df['Destination'] == '55 Cancri e') & (df['CabinDeck'] == 'D'), 'HomePlanet']='Europa'
    
    print('4. (after replacement through the Destination):', df.loc[df['HomePlanet'].isnull(), :].shape[0])
    #------------------------------------------------------------------------
    # für alle verbleibenden leeren Werte durch 'Earth' ersetzen
    df.fillna(value= {'HomePlanet': 'Earth'}, inplace=True)
    
    print('5. (after replacement by the most common value) HomePlanet NULLs:', df.loc[df['HomePlanet'].isnull(), :].shape[0])

    return df
X_train = HomePlanet_update(X_train, for_visual_df)

------ HomePlanet_update ------------
HomePlanet NULLs: 166
1. (after replacement through the GroupId) HomePlanet NULLs: 94
2. (after replacement through the Deck) HomePlanet NULLs: 54
3. (after replacement through the LastName) HomePlanet NULLs: 7
4. (after replacement through the Destination): 0
5. (after replacement by the most common value) HomePlanet NULLs: 0


for_visual_df.loc[
            for_visual_df['Destination'].notnull(), :].groupby(['LastName'], as_index=False).agg(
                                num_planets=('Destination', lambda x: len(x.unique())),
                                not_null_planet = ('Destination', lambda x: x.unique()[0])).sort_values('num_planets', ascending=False)


for_visual_df.loc[for_visual_df['Destination'].notnull(), :].groupby(['GroupId'], as_index=False).agg(
    num_planets=('Destination', lambda x: len(x.unique())),
    not_null_planet = ('Destination', lambda x: x.unique()[0])).sort_values('num_planets', ascending=False)


def Destination_update(df):
    print('------ Destination_update ------------')
    start_nulls_num = df.loc[df['Destination'].isnull(), :].shape[0]
 
    print('Destination NULLs:', start_nulls_num)
    
    df.fillna(value= {'Destination': 'TRAPPIST-1e'}, inplace=True)
    
    print('(after replacement by the most common value) Destination NULLs:', df.loc[df['Destination'].isnull(), :].shape[0])
    
    return df
    
X_train= Destination_update(X_train)

------ Destination_update ------------
Destination NULLs: 153
(after replacement by the most common value) Destination NULLs: 0


num_unique_last_names_in_group = for_visual_df[for_visual_df['GroupSize'] > 1].groupby(['GroupId'], as_index=False).agg(
                lastname_num_in_group = ('LastName', lambda x: len(x.value_counts()))).groupby('lastname_num_in_group').size().sort_values()
num_unique_last_names_in_group

lastname_num_in_group
4       1
3      23
2     225
1    1163
dtype: int64


# Countplot of unique values
plt.figure(figsize=(10,4))
sns.barplot(x=num_unique_last_names_in_group.index, y=num_unique_last_names_in_group.values)
plt.title('Number of unique surnames by group')

Text(0.5, 1.0, 'Number of unique surnames by group')


for_visual_df.loc[for_visual_df['LastName'].notnull(), ['GroupId', 'LastName']].groupby(['GroupId'], as_index=False).agg(
                        not_null_last_name = ('LastName', lambda x: x.value_counts().index[0]))


# suchen nach LastName in der gleichen Gruppe und aktualisiren null Werte

def LastName_update(df):
    print('------ LastName_update ------------')
    print('LastName NULLs:', df.loc[df['LastName'].isnull()].shape[0])
          
    lastnames_for_nulls_df = df.loc[df['LastName'].notnull(), ['GroupId', 'LastName']].groupby(['GroupId'], as_index=False).agg(
                        not_null_last_name = ('LastName', lambda x: x.value_counts().index[0]))  
    print(lastnames_for_nulls_df.shape)
          
    df['LastName'] = df.apply(
            lambda x: (
                lastnames_for_nulls_df.loc[lastnames_for_nulls_df['GroupId'] == x.GroupId, 'not_null_last_name'].iloc[0]
                    if lastnames_for_nulls_df.loc[lastnames_for_nulls_df['GroupId'] == x.GroupId, 'not_null_last_name'].shape[0] > 0 else np.nan)
                if x.LastName is np.nan else x.LastName , axis=1)      
          
    print('(after update) LastName NULLs:', df.loc[df['LastName'].isnull()].shape[0])
    
    #--------------------------------------------------------
    # die abhängigen Felder aktualisieren
    namesakes_in_group_df = df.groupby(['LastName', 'GroupId'], as_index=False).agg(
                namesakes_num_in_group = ('PassengerId', 'count'))

    df = df.merge(
        namesakes_in_group_df, how='left',
        left_on=['LastName', 'GroupId'],
        right_on=['LastName', 'GroupId'])

    df['namesakes_num_in_group'] = df['namesakes_num_in_group'] - 1
    
    print('namesakes_num_in_group NULLs:', df.loc[df['namesakes_num_in_group'].isnull()].shape[0])
    
    df.fillna(value= {'namesakes_num_in_group': 0}, inplace=True)
    
    print('namesakes_num_in_group NULLs:', df.loc[df['namesakes_num_in_group'].isnull()].shape[0])
    
    #----------------------------------------------------------------
    
    df.loc[df['Name'].isnull(), 'Name'] =  df.loc[df['Name'].isnull(), 'LastName']
    df['NameLength'] = df.loc[:, 'Name'].str.len()
    df.fillna(value= {'NameLength': 0}, inplace=True)
    
    return df


X_train = LastName_update(X_train)

------ LastName_update ------------
LastName NULLs: 165
(5127, 2)
(after update) LastName NULLs: 95
namesakes_num_in_group NULLs: 95
namesakes_num_in_group NULLs: 0


tmp = X_train.loc[X_train['CabinDeck'].notnull(), ['GroupId', 'CabinDeck']].groupby(['GroupId'], as_index=False).agg(
                        not_null_cabin_deck = ('CabinDeck', lambda x: len(x.value_counts()))).sort_values('not_null_cabin_deck')  


tmp.groupby('not_null_cabin_deck').size()

not_null_cabin_deck
1    4826
2     300
3      13
dtype: int64


def CabinDeck_update(df):
    print('------ CabinDeck_update ------------')
    print('CabinDeck NULLs:', df.loc[df['CabinDeck'].isnull()].shape[0])
    
    cabindecks_for_nulls_df = df.loc[df['CabinDeck'].notnull(), ['GroupId', 'CabinDeck']].groupby(['GroupId'], as_index=False).agg(
                        not_null_cabin_deck = ('CabinDeck', lambda x: x.value_counts().index[0])).sort_values('not_null_cabin_deck')
    
    
    df['CabinDeck'] = df.apply(
            lambda x: (
                cabindecks_for_nulls_df.loc[cabindecks_for_nulls_df['GroupId'] == x.GroupId, 'not_null_cabin_deck'].iloc[0]
                    if cabindecks_for_nulls_df.loc[cabindecks_for_nulls_df['GroupId'] == x.GroupId, 'not_null_cabin_deck'].shape[0] > 0 else np.nan)
                if x.CabinDeck is np.nan else x.CabinDeck , axis=1)      
          
    
    print('(after update throw GroupId) CabinDeck NULLs:', df.loc[df['CabinDeck'].isnull()].shape[0])
    
    #---------------------------------------------------------------------------------
    
#     Mars -> F
#     Europa -> B
#     Earth -> G

    df.loc[(df['CabinDeck'].isnull()) & (df['HomePlanet'] == 'Mars'), 'CabinDeck'] = 'F'
    df.loc[(df['CabinDeck'].isnull()) & (df['HomePlanet'] == 'Earth'), 'CabinDeck'] = 'G'
    df.loc[(df['CabinDeck'].isnull()) & (df['HomePlanet'] == 'Europa'), 'CabinDeck'] = 'B'
    print('(after update throw HomePlanet) CabinDeck NULLs:', df.loc[df['CabinDeck'].isnull()].shape[0])
    
    return df

X_train = CabinDeck_update(X_train)

------ CabinDeck_update ------------
CabinDeck NULLs: 157
(after update throw GroupId) CabinDeck NULLs: 83
(after update throw HomePlanet) CabinDeck NULLs: 0


decks = sorted(list(for_visual_df.CabinDeck.value_counts().index))

decks

['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T']


def CabinNum_update(df, source_data):
    print('------ CabinNum_update ------------')
    null_ind = df.loc[df['CabinNum'].isnull()].index
    
    
    print('CabinNum NULLs:', df.loc[df['CabinNum'].isnull()].shape[0])
    decks = sorted(list(source_data.CabinDeck.value_counts().index))
    print(decks)
    
    df_y_pred= []
    
    for deck in decks:
        df_x_pred = df.loc[(df['CabinNum'].isnull()) & (df['CabinDeck'] == deck), ['GroupId']]
        
        if df_x_pred.shape[0] > 0:
            
            df_x_train = df.loc[(df['CabinNum'].notnull()) & (df['CabinDeck'] == deck), ['GroupId']]
            df_y_train = df.loc[(df['CabinNum'].notnull()) & (df['CabinDeck'] == deck), ['CabinNum']]
        

            lr_mod = LinearRegression()
            lr_mod.fit(df_x_train, df_y_train)
            df_y_pred = lr_mod.predict(df_x_pred)
        
            print(deck, df_y_pred.shape)
        
        
            df.loc[(df['CabinNum'].isnull()) & (df['CabinDeck'] == deck), ['CabinNum']] = df_y_pred
    print('CabinNum NULLs:', df.loc[df['CabinNum'].isnull()].shape[0])
    
    return (null_ind, df)
        
null_cabin_num_ind, X_train = CabinNum_update(X_train, for_visual_df)

------ CabinNum_update ------------
CabinNum NULLs: 157
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T']
A (6, 1)
B (29, 1)
C (14, 1)
D (5, 1)
E (6, 1)
F (31, 1)
G (66, 1)
CabinNum NULLs: 0


fig, axs = plt.subplots(figsize=(12, 6))

sns.scatterplot(data=X_train.loc[null_cabin_num_ind, :], x='CabinNum', y='GroupId', hue='CabinDeck', ax=axs)

<AxesSubplot:xlabel='CabinNum', ylabel='GroupId'>


tmp = X_train.loc[X_train['CabinSide'].notnull(), ['GroupId', 'CabinSide']].groupby(['GroupId'], as_index=False).agg(
                        not_null_cabin_side = ('CabinSide', lambda x: len(x.value_counts()))).sort_values('not_null_cabin_side')  


tmp.groupby('not_null_cabin_side').size()

not_null_cabin_side
1    5139
dtype: int64


def CabinSide_update(df, source_data):
    print('------ CabinSide_update ------------')
    print('CabinSide NULLs:', df.loc[df['CabinSide'].isnull()].shape[0])
    
    cabin_side_for_group_id_df = source_data.loc[
            source_data['CabinSide'].notnull(), :].groupby(['GroupId'], as_index=False).agg(
                                not_null_cabin_side = ('CabinSide', lambda x: x.unique()[0]))
    
    df['CabinSide'] = df.apply(
    lambda x: 
        (cabin_side_for_group_id_df.loc[cabin_side_for_group_id_df['GroupId'] == x.GroupId, 'not_null_cabin_side'].iloc[0] if cabin_side_for_group_id_df.loc[cabin_side_for_group_id_df['GroupId'] == x.GroupId, 'not_null_cabin_side'].shape[0] > 0 else np.nan)
        if x.CabinSide is np.nan else x.CabinSide , axis=1)
    
    print('CabinSide NULLs:', df.loc[df['CabinSide'].isnull()].shape[0])
    
    #-----------------------------------------------------
    
    df.fillna(value= {'CabinSide': 'O'}, inplace=True)
    print('CabinSide NULLs:', df.loc[df['CabinSide'].isnull()].shape[0])
    
    return df
    
X_train = CabinSide_update(X_train, for_visual_df)

------ CabinSide_update ------------
CabinSide NULLs: 157
CabinSide NULLs: 81
CabinSide NULLs: 0


X_train.CabinSide.unique()

array(['S', 'P', 'O'], dtype=object)


def VIP_update(df):
    print('------ VIP_update ------------')
    print('VIP NULLs:', df.loc[df['VIP'].isnull()].shape[0])
    
    df.fillna(value= {'VIP': False}, inplace=True)
    
    print('VIP NULLs:', df.loc[df['VIP'].isnull()].shape[0])
    
    return df
    
    
X_train = VIP_update(X_train)

------ VIP_update ------------
VIP NULLs: 166
VIP NULLs: 0


for_visual_df.loc[
    (for_visual_df['CryoSleep'].notnull())
    & (for_visual_df['CryoSleep'])
    & (for_visual_df['TotalSpend'].notnull()), 'TotalSpend'].sum()

0.0


def CryoSleep_update(df):
    print('------ CryoSleep_update ------------')
    print(df.loc[df['CryoSleep'].isnull(), :].shape[0])
    
    df.loc[(df['CryoSleep'].isnull()) & (df['TotalSpend'] == 0), 'CryoSleep'] = True
    
    print(df.loc[df['CryoSleep'].isnull(), :].shape[0])
    
    #------------------------------------------------------------
    
    df.fillna(value= {'CryoSleep': False}, inplace=True)
    print(df.loc[df['CryoSleep'].isnull(), :].shape[0])
    
    return df
    
X_train = CryoSleep_update(X_train)

------ CryoSleep_update ------------
165
101
0


def get_age_medians(df):

    return df.loc[:, ['IsSingle', 'HomePlanet', 'VIP', 'Age']].groupby(
        ['IsSingle', 'HomePlanet', 'VIP'], 
        as_index=False).agg('median')


def Age_update(df, median_values):
    print('------ Age_update ------------')
    print('Nulls in Expenses:', df.loc[:, 'Age'].isna().sum())
    
    for index, row in median_values.iterrows():
        
        cond = (df['IsSingle'] == row['IsSingle']) & (df['VIP'] == row['VIP']) & (df['HomePlanet'] == row['HomePlanet'])
        
        df.loc[cond, :] = df.loc[cond, :].fillna(value = {'Age': row['Age']})

    print('Nulls in Expenses:', df.loc[:, 'Age'].isna().sum())   
    
    #-----------------------------------------------------------------------------
    
    df.fillna(value= {'Age': 0}, inplace=True)
    print('Nulls in Expenses:', df.loc[:, 'Age'].isna().sum())
    
    return df

age_medians_train = get_age_medians(X_train)

X_train = Age_update(X_train, age_medians_train)

------ Age_update ------------
Nulls in Expenses: 154
Nulls in Expenses: 0
Nulls in Expenses: 0


group_cols = ['IsSingle', 'VIP', 'HomePlanet']
cols= expenses_cols + group_cols

not_child_not_sleep_df = X_train.loc[(X_train['Age'] >= 13) & (X_train['CryoSleep'] == False), :]
print(not_child_not_sleep_df.shape)


no_spend_df = not_child_not_sleep_df.loc[
    (not_child_not_sleep_df['ShoppingMall']==0) &
    (not_child_not_sleep_df['FoodCourt']==0) &
    (not_child_not_sleep_df['RoomService']==0) &
    (not_child_not_sleep_df['Spa']==0) &
    (not_child_not_sleep_df['VRDeck']==0), :
]

print(no_spend_df.shape)
    
expenses_means = not_child_not_sleep_df.loc[:, cols].groupby(group_cols, as_index=False).agg('mean')
expenses_means

(4122, 26)
(80, 26)


expenses_medians = not_child_not_sleep_df.loc[:, cols].groupby(group_cols, as_index=False).agg('median')
expenses_medians


for index, row in expenses_medians.iterrows():
    print(row[expenses_cols].to_dict())
    print('---------------------------')

{'RoomService': 5.0, 'FoodCourt': 4.0, 'ShoppingMall': 4.0, 'Spa': 2.0, 'VRDeck': 7.0}
---------------------------
{'RoomService': 0.0, 'FoodCourt': 1421.0, 'ShoppingMall': 0.0, 'Spa': 445.0, 'VRDeck': 473.0}
---------------------------
{'RoomService': 853.0, 'FoodCourt': 0.0, 'ShoppingMall': 170.0, 'Spa': 0.0, 'VRDeck': 0.0}
---------------------------
{'RoomService': 0.0, 'FoodCourt': 1525.0, 'ShoppingMall': 0.0, 'Spa': 417.0, 'VRDeck': 1547.0}
---------------------------
{'RoomService': 788.0, 'FoodCourt': 0.0, 'ShoppingMall': 354.0, 'Spa': 4.0, 'VRDeck': 0.0}
---------------------------
{'RoomService': 3.0, 'FoodCourt': 4.0, 'ShoppingMall': 4.0, 'Spa': 6.0, 'VRDeck': 4.0}
---------------------------
{'RoomService': 0.0, 'FoodCourt': 1389.0, 'ShoppingMall': 0.0, 'Spa': 272.5, 'VRDeck': 438.0}
---------------------------
{'RoomService': 726.5, 'FoodCourt': 0.0, 'ShoppingMall': 268.0, 'Spa': 0.0, 'VRDeck': 0.0}
---------------------------
{'RoomService': 0.0, 'FoodCourt': 2343.0, 'ShoppingMall': 0.0, 'Spa': 269.0, 'VRDeck': 680.0}
---------------------------
{'RoomService': 687.5, 'FoodCourt': 0.0, 'ShoppingMall': 177.0, 'Spa': 0.0, 'VRDeck': 0.0}
---------------------------


def get_expenses_means(df):
    not_child_not_sleep_df = df.loc[(X_train['Age'] >= 13) & (df['CryoSleep'] == False), :]
    
    expenses_means = not_child_not_sleep_df.loc[:, cols].groupby(group_cols, as_index=False).agg('mean')
    return expenses_means


def Expenses_update(df, median_values):
    print('------ Expenses_update ------------')
    values = {"RoomService": 0, "FoodCourt": 0, "ShoppingMall": 0, "Spa": 0, 'VRDeck': 0}
    

    print('Nulls in Expenses:', df.loc[:, expenses_cols].isna().sum().sum(axis = 0))
    
    df.loc[(df['Age'] < 13), :] = df.loc[(df['Age'] < 13), :].fillna(value = values)
    
    print('Nulls in Expenses:', df.loc[:, expenses_cols].isna().sum().sum(axis = 0))
    #-------------------------------------------------------------------------------
    
    df.loc[(df['CryoSleep'].notnull())
           & (df['CryoSleep']), :] = df.loc[(df['CryoSleep'].notnull()) & (df['CryoSleep']), :].fillna(value = values)
    
    print('Nulls in Expenses:', df.loc[:, expenses_cols].isna().sum().sum(axis = 0))
    
    #-------------------------------------------------------------------------
    
    for index, row in median_values.iterrows():
        
        cond = (df['IsSingle'] == row['IsSingle']) & (df['VIP'] == row['VIP']) & (df['HomePlanet'] == row['HomePlanet'])
        
        tmp = df.loc[cond, expenses_cols]
        print(tmp.shape)
        
        df.loc[cond, :] = df.loc[cond, :].fillna(value = row[expenses_cols].to_dict())

    print('Nulls in Expenses:', df.loc[:, expenses_cols].isna().sum().sum(axis = 0))   
    
    #-----------------------------------------------------------------------------
    
    df.fillna(value= values, inplace=True)
    print('Nulls in Expenses:', df.loc[:, expenses_cols].isna().sum().sum(axis = 0))
    
    return df

expenses_means_train = get_expenses_means(X_train)

X_train = Expenses_update(X_train, expenses_means_train)

------ Expenses_update ------------
Nulls in Expenses: 755
Nulls in Expenses: 681
Nulls in Expenses: 426
(1177, 5)
(923, 5)
(615, 5)
(64, 5)
(21, 5)
(2624, 5)
(683, 5)
(770, 5)
(44, 5)
(33, 5)
Nulls in Expenses: 0
Nulls in Expenses: 0


def log_expenses(df):
    print('------ log_expenses ------------')
    num_features = ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck' ]

    for f in num_features:
        df[f] = np.log10(df[f] + 1)
        
    return df
        
X_train = log_expenses(X_train)

------ log_expenses ------------


def New_features_update(df):
    print('------ New_features_update ------------')
    df['TotalSpend'] = df.loc[:, expenses_cols].sum(axis=1)
    df['NoSpend'] = df.loc[:, 'TotalSpend'].apply(lambda x: np.nan if math.isnan(x) else x==0)
    df['IsChild'] = df.loc[:, 'Age'] < 18
    df['Route'] = df['HomePlanet'] + ' - ' + df['Destination']
    
    return df
    
X_train = New_features_update(X_train)

------ New_features_update ------------


X_train.isnull().sum()

PassengerId                 0
HomePlanet                  0
CryoSleep                   0
Cabin                     157
Destination                 0
Age                         0
VIP                         0
RoomService                 0
FoodCourt                   0
ShoppingMall                0
Spa                         0
VRDeck                      0
Name                       95
FirstName                 165
LastName                   95
GroupId                     0
NumInGroup                  0
CabinDeck                   0
CabinNum                    0
CabinSide                   0
IsChild                     0
TotalSpend                  0
GroupSize                   0
IsSingle                    0
namesakes_num_in_group      0
NameLength                  0
NoSpend                     0
Route                       0
dtype: int64


X_train.drop(['Cabin', 'PassengerId', 'Name', 'FirstName', 'LastName'], axis=1, inplace=True)


X_train.describe()


X_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6954 entries, 0 to 6953
Data columns (total 23 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   HomePlanet              6954 non-null   object 
 1   CryoSleep               6954 non-null   bool   
 2   Destination             6954 non-null   object 
 3   Age                     6954 non-null   float64
 4   VIP                     6954 non-null   bool   
 5   RoomService             6954 non-null   float64
 6   FoodCourt               6954 non-null   float64
 7   ShoppingMall            6954 non-null   float64
 8   Spa                     6954 non-null   float64
 9   VRDeck                  6954 non-null   float64
 10  GroupId                 6954 non-null   float64
 11  NumInGroup              6954 non-null   float64
 12  CabinDeck               6954 non-null   object 
 13  CabinNum                6954 non-null   float64
 14  CabinSide               6954 non-null   object 
 15  IsChild                 6954 non-null   bool   
 16  TotalSpend              6954 non-null   float64
 17  GroupSize               6954 non-null   int64  
 18  IsSingle                6954 non-null   bool   
 19  namesakes_num_in_group  6954 non-null   float64
 20  NameLength              6954 non-null   float64
 21  NoSpend                 6954 non-null   bool   
 22  Route                   6954 non-null   object 
dtypes: bool(5), float64(12), int64(1), object(5)
memory usage: 1.3+ MB


def bool_to_int(df):
    print('------ bool_to_int ------------')
    df['CryoSleep'] = df["CryoSleep"].astype(int)
    df['VIP'] = df["VIP"].astype(int)
    df['IsChild'] = df["IsChild"].astype(int)
    df['IsSingle'] = df["IsSingle"].astype(int)
    df['NoSpend'] = df["NoSpend"].astype(int)
    
    return df

X_train = bool_to_int(X_train)

------ bool_to_int ------------


# ---------------------------------------------------------------------------------------------


df_test = pd.read_csv("test.csv", sep=',', engine='python')
df_test


df_test[['GroupId', 'NumInGroup']] = df_test['PassengerId'].str.split('_', expand=True)
df_test['GroupId'] = df_test.loc[:,'GroupId'].astype(float)


pd.merge(X_train, df_test, how ='inner', on =['GroupId', 'GroupId'])


def encode_data(df):
    numeric_selector = make_column_selector(dtype_include=np.number)
    numeric_columns = numeric_selector(X_train)
    print(numeric_columns)

    
    nominal_columns = ["HomePlanet", "Destination", 'CabinSide', 'CabinDeck', 'Route']
    nominal_categories = [list(sorted(X_train[column].unique())) for column in nominal_columns]

    print(nominal_categories)

    nominal_encoder = OneHotEncoder(categories=nominal_categories, sparse=False)
    nominal_encoder.fit(X_train.loc[:, nominal_columns])
    X_train_nominal = nominal_encoder.transform(X_train.loc[:, nominal_columns])
    X_train_nominal = pd.DataFrame(
        X_train_nominal,
        columns=nominal_encoder.get_feature_names(),
        index=X_train.index
    )
    
    X_train = pd.concat((X_train_nominal, X_train.drop(columns=nominal_columns)), axis=1)

    # numerische features skalieren
    scaler = MinMaxScaler()
    scaler.fit(X_train[numeric_columns])
    X_train.loc[:, numeric_columns] = scaler.transform(X_train[numeric_columns])

    # Box Plot erzeugen
    plt.figure(figsize=(15, 8))
    X_train.loc[:, numeric_columns].boxplot(rot=90)


numeric_selector = make_column_selector(dtype_include=np.number)
numeric_columns = numeric_selector(X_train)
numeric_columns

['CryoSleep',
 'Age',
 'VIP',
 'RoomService',
 'FoodCourt',
 'ShoppingMall',
 'Spa',
 'VRDeck',
 'GroupId',
 'NumInGroup',
 'CabinNum',
 'IsChild',
 'TotalSpend',
 'GroupSize',
 'IsSingle',
 'namesakes_num_in_group',
 'NameLength',
 'NoSpend']


nominal_columns = ["HomePlanet", "Destination", 'CabinSide', 'CabinDeck', 'Route']
nominal_categories = [list(sorted(X_train[column].unique())) for column in nominal_columns]

print(nominal_categories)

nominal_encoder = OneHotEncoder(categories=nominal_categories, sparse=False)
nominal_encoder.fit(X_train.loc[:, nominal_columns])
X_train_nominal = nominal_encoder.transform(X_train.loc[:, nominal_columns])
X_train_nominal = pd.DataFrame(
    X_train_nominal,
    columns=nominal_encoder.get_feature_names(),
    index=X_train.index
)
X_train_nominal

[['Earth', 'Europa', 'Mars'], ['55 Cancri e', 'PSO J318.5-22', 'TRAPPIST-1e'], ['O', 'P', 'S'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T'], ['Earth - 55 Cancri e', 'Earth - PSO J318.5-22', 'Earth - TRAPPIST-1e', 'Europa - 55 Cancri e', 'Europa - PSO J318.5-22', 'Europa - TRAPPIST-1e', 'Mars - 55 Cancri e', 'Mars - PSO J318.5-22', 'Mars - TRAPPIST-1e']]


X_train = pd.concat((X_train_nominal, X_train.drop(columns=nominal_columns)), axis=1)
X_train.head()


# numeric_columns.append('CabinDeck')

# Box Plot erzeugen
plt.figure(figsize=(15, 8))
X_train[numeric_columns].boxplot(rot=90)

<AxesSubplot:>


# numerische features skalieren
scaler = MinMaxScaler()
scaler.fit(X_train[numeric_columns])
X_train.loc[:, numeric_columns] = scaler.transform(X_train[numeric_columns])

# Box Plot erzeugen
plt.figure(figsize=(15, 8))
X_train.loc[:, numeric_columns].boxplot(rot=90)

<AxesSubplot:>


X_train


X_train.dtypes

x0_Earth                     float64
x0_Europa                    float64
x0_Mars                      float64
x1_55 Cancri e               float64
x1_PSO J318.5-22             float64
x1_TRAPPIST-1e               float64
x2_O                         float64
x2_P                         float64
x2_S                         float64
x3_A                         float64
x3_B                         float64
x3_C                         float64
x3_D                         float64
x3_E                         float64
x3_F                         float64
x3_G                         float64
x3_T                         float64
x4_Earth - 55 Cancri e       float64
x4_Earth - PSO J318.5-22     float64
x4_Earth - TRAPPIST-1e       float64
x4_Europa - 55 Cancri e      float64
x4_Europa - PSO J318.5-22    float64
x4_Europa - TRAPPIST-1e      float64
x4_Mars - 55 Cancri e        float64
x4_Mars - PSO J318.5-22      float64
x4_Mars - TRAPPIST-1e        float64
CryoSleep                    float64
Age                          float64
VIP                          float64
RoomService                  float64
FoodCourt                    float64
ShoppingMall                 float64
Spa                          float64
VRDeck                       float64
GroupId                      float64
NumInGroup                   float64
CabinNum                     float64
IsChild                      float64
TotalSpend                   float64
GroupSize                    float64
IsSingle                     float64
namesakes_num_in_group       float64
NameLength                   float64
NoSpend                      float64
dtype: object


X_train.memory_usage(deep= True)

Index                        319864
x0_Earth                      55632
x0_Europa                     55632
x0_Mars                       55632
x1_55 Cancri e                55632
x1_PSO J318.5-22              55632
x1_TRAPPIST-1e                55632
x2_O                          55632
x2_P                          55632
x2_S                          55632
x3_A                          55632
x3_B                          55632
x3_C                          55632
x3_D                          55632
x3_E                          55632
x3_F                          55632
x3_G                          55632
x3_T                          55632
x4_Earth - 55 Cancri e        55632
x4_Earth - PSO J318.5-22      55632
x4_Earth - TRAPPIST-1e        55632
x4_Europa - 55 Cancri e       55632
x4_Europa - PSO J318.5-22     55632
x4_Europa - TRAPPIST-1e       55632
x4_Mars - 55 Cancri e         55632
x4_Mars - PSO J318.5-22       55632
x4_Mars - TRAPPIST-1e         55632
CryoSleep                     55632
Age                           55632
VIP                           55632
RoomService                   55632
FoodCourt                     55632
ShoppingMall                  55632
Spa                           55632
VRDeck                        55632
GroupId                       55632
NumInGroup                    55632
CabinNum                      55632
IsChild                       55632
TotalSpend                    55632
GroupSize                     55632
IsSingle                      55632
namesakes_num_in_group        55632
NameLength                    55632
NoSpend                       55632
dtype: int64


def memory_optimiz(df):
    print('------ memory_optimiz ------------')
    df_opti = df.apply(pd.to_numeric, downcast="float")
    
    reduction = (df.memory_usage(deep=True).sum()-df_opti.memory_usage(deep=True).sum())/df.memory_usage(deep=True).sum() *  100

    print(f"Reduction = {reduction:0.2f}%")
    
    return df_opti


X_train = memory_optimiz(X_train)

------ memory_optimiz ------------
Reduction = 44.22%


X_train_tmp = X_train.copy()
X_train_tmp['Transported'] = y_train

corrmat = X_train_tmp.corr()

fig = plt.figure(figsize=(25, 25))
sns.heatmap(corrmat, vmax=1, vmin=-1, square=True, cmap="seismic", annot=True)

<AxesSubplot:>


def data_transform(df, source_data, *args):
    
    age_medians = []
    expenses_means = []
    df = new_features_create(df)
    
    df = HomePlanet_update(df, source_data)
    df= Destination_update(df)
    df = LastName_update(df)
    df = CabinDeck_update(df)   
    null_cabin_num_ind, df = CabinNum_update(df, source_data)
    df = CabinSide_update(df, source_data)
    df = VIP_update(df)
    df = CryoSleep_update(df)
      
    if len(args) == 0:
        age_medians = get_age_medians(df)
        expenses_means = get_expenses_means(df)
    else:
        age_medians = args[0]
        expenses_means = args[1]  
    
    df = Age_update(df, age_medians)
    df = Expenses_update(df, expenses_means)
        
    df = log_expenses(df)
    df = New_features_update(df)

    df.drop(['Cabin', 'PassengerId', 'Name', 'FirstName', 'LastName'], axis=1, inplace=True)
    
    df = bool_to_int(df)
    
    return df, age_medians, expenses_means


def encode_data(df, nominal_encoder, scaler):

    df_nominal = nominal_encoder.transform(df.loc[:, nominal_columns])
    df_nominal = pd.DataFrame(
        df_nominal,
        columns=nominal_encoder.get_feature_names(),
        index=df.index
    )
    df_nominal


    df = pd.concat((df_nominal, df.drop(columns=nominal_columns)), axis=1)


    df.loc[:, numeric_columns] = scaler.transform(df[numeric_columns])
    
    return df


X_test, am, em = data_transform(X_test, for_visual_df, age_medians_train, expenses_means_train)
X_test = encode_data(X_test, nominal_encoder, scaler)
X_test = memory_optimiz(X_test)

------ HomePlanet_update ------------
HomePlanet NULLs: 35
1. (after replacement through the GroupId) HomePlanet NULLs: 17
2. (after replacement through the Deck) HomePlanet NULLs: 9
3. (after replacement through the LastName) HomePlanet NULLs: 1
4. (after replacement through the Destination): 0
5. (after replacement by the most common value) HomePlanet NULLs: 0
------ Destination_update ------------
Destination NULLs: 29
(after replacement by the most common value) Destination NULLs: 0
------ LastName_update ------------
LastName NULLs: 35
(1554, 2)
(after update) LastName NULLs: 29
namesakes_num_in_group NULLs: 29
namesakes_num_in_group NULLs: 0
------ CabinDeck_update ------------
CabinDeck NULLs: 42
(after update throw GroupId) CabinDeck NULLs: 30
(after update throw HomePlanet) CabinDeck NULLs: 0
------ CabinNum_update ------------
CabinNum NULLs: 42
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T']
B (10, 1)
F (8, 1)
G (24, 1)
CabinNum NULLs: 0
------ CabinSide_update ------------
CabinSide NULLs: 42
CabinSide NULLs: 18
CabinSide NULLs: 0
------ VIP_update ------------
VIP NULLs: 37
VIP NULLs: 0
------ CryoSleep_update ------------
52
29
0
------ Age_update ------------
Nulls in Expenses: 25
Nulls in Expenses: 0
Nulls in Expenses: 0
------ Expenses_update ------------
Nulls in Expenses: 188
Nulls in Expenses: 172
Nulls in Expenses: 111
(121, 5)
(103, 5)
(58, 5)
(7, 5)
(0, 5)
(792, 5)
(332, 5)
(296, 5)
(18, 5)
(12, 5)
Nulls in Expenses: 0
Nulls in Expenses: 0
------ log_expenses ------------
------ New_features_update ------------
------ bool_to_int ------------
------ memory_optimiz ------------
Reduction = 48.89%


data = pd.read_csv("train.csv", sep=',', engine='python')


X, age_medians_all, expenses_means_all = data_transform(data.drop(columns="Transported"), for_visual_df)
y = data["Transported"].astype(int)

------ HomePlanet_update ------------
HomePlanet NULLs: 201
1. (after replacement through the GroupId) HomePlanet NULLs: 111
2. (after replacement through the Deck) HomePlanet NULLs: 63
3. (after replacement through the LastName) HomePlanet NULLs: 8
4. (after replacement through the Destination): 0
5. (after replacement by the most common value) HomePlanet NULLs: 0
------ Destination_update ------------
Destination NULLs: 182
(after replacement by the most common value) Destination NULLs: 0
------ LastName_update ------------
LastName NULLs: 200
(6113, 2)
(after update) LastName NULLs: 104
namesakes_num_in_group NULLs: 104
namesakes_num_in_group NULLs: 0
------ CabinDeck_update ------------
CabinDeck NULLs: 199
(after update throw GroupId) CabinDeck NULLs: 99
(after update throw HomePlanet) CabinDeck NULLs: 0
------ CabinNum_update ------------
CabinNum NULLs: 199
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T']
A (6, 1)
B (38, 1)
C (14, 1)
D (9, 1)
E (8, 1)
F (36, 1)
G (88, 1)
CabinNum NULLs: 0
------ CabinSide_update ------------
CabinSide NULLs: 199
CabinSide NULLs: 99
CabinSide NULLs: 0
------ VIP_update ------------
VIP NULLs: 203
VIP NULLs: 0
------ CryoSleep_update ------------
217
130
0
------ Age_update ------------
Nulls in Expenses: 179
Nulls in Expenses: 0
Nulls in Expenses: 0
------ Expenses_update ------------
Nulls in Expenses: 943
Nulls in Expenses: 853
Nulls in Expenses: 537
Nulls in Expenses: 537
Nulls in Expenses: 0
------ log_expenses ------------
------ New_features_update ------------
------ bool_to_int ------------


def encode_all_data(df):
    numeric_selector = make_column_selector(dtype_include=np.number)
    numeric_columns = numeric_selector(df)
    print(numeric_columns)

    
    nominal_columns = ["HomePlanet", "Destination", 'CabinSide', 'CabinDeck', 'Route']
    nominal_categories = [list(sorted(df[column].unique())) for column in nominal_columns]

    print(nominal_categories)

    nominal_encoder = OneHotEncoder(categories=nominal_categories, sparse=False)
    nominal_encoder.fit(df.loc[:, nominal_columns])
    df_nominal = nominal_encoder.transform(df.loc[:, nominal_columns])
    df_nominal = pd.DataFrame(
        df_nominal,
        columns=nominal_encoder.get_feature_names(),
        index=df.index
    )
    
    df = pd.concat((df_nominal, df.drop(columns=nominal_columns)), axis=1)

    # numerische features skalieren
    scaler = MinMaxScaler()
    scaler.fit(df[numeric_columns])
    df.loc[:, numeric_columns] = scaler.transform(df[numeric_columns])
    
    return df, nominal_encoder, scaler


X, nominal_encoder_all, scaler_all = encode_all_data(X)

X = memory_optimiz(X)

['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'GroupId', 'NumInGroup', 'CabinNum', 'IsChild', 'TotalSpend', 'GroupSize', 'IsSingle', 'namesakes_num_in_group', 'NameLength', 'NoSpend']
[['Earth', 'Europa', 'Mars'], ['55 Cancri e', 'PSO J318.5-22', 'TRAPPIST-1e'], ['O', 'P', 'S'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T'], ['Earth - 55 Cancri e', 'Earth - PSO J318.5-22', 'Earth - TRAPPIST-1e', 'Europa - 55 Cancri e', 'Europa - PSO J318.5-22', 'Europa - TRAPPIST-1e', 'Mars - 55 Cancri e', 'Mars - PSO J318.5-22', 'Mars - TRAPPIST-1e']]
------ memory_optimiz ------------
Reduction = 45.08%


roc_curves = {}
model_acc = {}


def get_roc_curve(y_test, y_test_pred_prob):
    fpr, tpr, thresholds = roc_curve(y_test, y_test_pred_prob)
    rel = {}
    for i in range(0, len(fpr)):
        rel[i] = tpr[i] + (1 - fpr[i])
    
    best_threshold_ind = max(rel, key=rel.get)


    print('tpr =', tpr[best_threshold_ind], 'fpr =', fpr[best_threshold_ind], 'threshold =', thresholds[best_threshold_ind] )
  
    return fpr, tpr, best_threshold_ind, thresholds[best_threshold_ind]


def plot_roc_curve(fpr, tpr, best_threshold_ind):
    fig, axs = plt.subplots(figsize=(10, 6))


    plt.hlines(y=tpr[best_threshold_ind], xmin=0, xmax=fpr[best_threshold_ind], linestyles='--')
    plt.vlines(x=fpr[best_threshold_ind], ymin=0, ymax=tpr[best_threshold_ind], linestyles='--')

    plt.plot([0, 1], [0, 1], linestyle='dashed', color='red')
    plt.plot(fpr, tpr)

    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC curve')


param_grid = {
    'penalty': ['l1','l2'],
    'C': [0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2],
    'max_iter': [50, 100, 150]
}

logisticRegression_gridsearch_hp_tuning = GridSearchCV(
        LogisticRegression(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 4
)

logisticRegression_gridsearch_hp_tuning.fit(X_train, y_train)
print(logisticRegression_gridsearch_hp_tuning.best_params_)

print("best_score = ", logisticRegression_gridsearch_hp_tuning.best_score_)
print('acc X_train = ', logisticRegression_gridsearch_hp_tuning.score(X_train, y_train))

Fitting 5 folds for each of 48 candidates, totalling 240 fits

C:\Users\natalie\anaconda3\lib\site-packages\sklearn\model_selection\_search.py:918: UserWarning: One or more of the test scores are non-finite: [       nan 0.77149921        nan 0.77149921        nan 0.77149921
        nan 0.77538224        nan 0.77538224        nan 0.77538224
        nan 0.77595726        nan 0.77624503        nan 0.77624503
        nan 0.77610083        nan 0.7763886         nan 0.77653238
        nan 0.77552571        nan 0.77624482        nan 0.77624482
        nan 0.77595705        nan 0.77624472        nan 0.7763886
        nan 0.77595716        nan 0.77595716        nan 0.77595716
        nan 0.77581327        nan 0.77610104        nan 0.77581337]
  warnings.warn(

{'C': 0.75, 'max_iter': 150, 'penalty': 'l2'}
best_score =  0.7765323844447087
acc X_train =  0.7795513373597929


lg_reg_mod = LogisticRegression(C = 0.75, max_iter = 150, penalty = 'l2').fit(X_train, y_train)
print('Acc Train =', round(lg_reg_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(lg_reg_mod.score(X_test, y_test) * 100, 2))

model_acc['LogisticRegression'] = (round(lg_reg_mod.score(X_test, y_test) * 100, 2), lg_reg_mod)

y_test_pred_prob = pd.DataFrame(lg_reg_mod.predict_proba(X_test)[:, 1])
#y_test_pred_prob

Acc Train = 77.96
Acc Test = 78.32


fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['LogisticRegression'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.7886178861788617 fpr = 0.2152619589977221 threshold = 0.5435228456093995


# pr, re, thres = precision_recall_curve( y_test, y_test_pred_prob)

# fig, axs = plt.subplots(figsize=(12, 6))
# plt.plot(re, pr)
# plt.xlabel('recall')
# plt.ylabel('precision')


#y_test_pred_prob

y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.544 = 78.6084


param_grid = {
    'max_depth': [3, 5, 10],
    'min_samples_leaf': [1, 3, 5, 8]
}

decisionTreeClassifier_gridsearch_hp_tuning = GridSearchCV(
        DecisionTreeClassifier(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 4
)

decisionTreeClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(decisionTreeClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", decisionTreeClassifier_gridsearch_hp_tuning.best_score_)
print('acc X_train = ', decisionTreeClassifier_gridsearch_hp_tuning.score(X_train, y_train))

Fitting 5 folds for each of 12 candidates, totalling 60 fits
{'max_depth': 5, 'min_samples_leaf': 3}
best_score =  0.780416242132103
acc X_train =  0.7985332182916307


dt_mod = DecisionTreeClassifier(max_depth = 5, min_samples_leaf = 3).fit(X_train, y_train)
print('Acc Train =', round(dt_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(dt_mod.score(X_test, y_test) * 100, 2))

model_acc['DecisionTreeClassifier'] = (round(dt_mod.score(X_test, y_test) * 100, 2), dt_mod)

y_test_pred_prob = pd.DataFrame(dt_mod.predict_proba(X_test)[:, 1])

Acc Train = 79.85
Acc Test = 78.55


fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['DecisionTreeClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8130081300813008 fpr = 0.22209567198177677 threshold = 0.5393258426966292


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.539 = 78.6084


param_grid = {
    'max_depth': [3, 5, 10],
    'min_samples_leaf': [1, 3, 5, 8],
    'n_estimators': [50, 100, 150, 200, 250, 300]
}

randomForestClassifier_gridsearch_hp_tuning = GridSearchCV(
        RandomForestClassifier(),
        param_grid=param_grid,
        n_jobs=-1,
        verbose = 4
)

randomForestClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(randomForestClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", randomForestClassifier_gridsearch_hp_tuning.best_score_)
print('acc X_train = ', randomForestClassifier_gridsearch_hp_tuning.score(X_train, y_train))

Fitting 5 folds for each of 72 candidates, totalling 360 fits
{'max_depth': 10, 'min_samples_leaf': 1, 'n_estimators': 250}
best_score =  0.8012674490170625
acc X_train =  0.8809318377911993


rf_mod = RandomForestClassifier(max_depth=10, min_samples_leaf=1, n_estimators = 250)
rf_mod.fit(X_train, y_train)

print('Acc Train =', round(rf_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(rf_mod.score(X_test, y_test) * 100, 2))

model_acc['RandomForestClassifier'] = (round(rf_mod.score(X_test, y_test) * 100, 2), rf_mod)

d = {'Features': X_train.columns, 'importance': rf_mod.feature_importances_*100}
features_imp_df = pd.DataFrame(data=d).sort_values('importance', ascending=False)
features_imp_df

Acc Train = 87.92
Acc Test = 80.85


y_test_pred_prob = pd.DataFrame(rf_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['RandomForestClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.7921022067363531 fpr = 0.1662870159453303 threshold = 0.5199264252758352


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.52 = 81.25359


# min_samples_leaf=[3, 5, 8]

param_grid = {
    "learning_rate": [0.01, 0.05, 0.1, 0.2, 0.5],
    "n_estimators": [50, 100, 150, 200, 250],
    'max_depth' : [3, 5, 8],
    'min_samples_leaf' : [1, 3, 5, 8]
}


gradientBoostingClassifier_gridsearch_hp_tuning = GridSearchCV(
        GradientBoostingClassifier(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 2
)

gradientBoostingClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(gradientBoostingClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", gradientBoostingClassifier_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 300 candidates, totalling 1500 fits
{'learning_rate': 0.1, 'max_depth': 3, 'min_samples_leaf': 5, 'n_estimators': 200}
best_score =  0.8065877765077657


gb_mod = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, max_depth=3, min_samples_leaf=5) 

gb_mod.fit(X_train, y_train)

print('Acc Train =', round(gb_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(gb_mod.score(X_test, y_test) * 100, 2))

model_acc['GradientBoostingClassifier'] = (round(gb_mod.score(X_test, y_test) * 100, 2), gb_mod)

Acc Train = 85.1
Acc Test = 81.14


y_test_pred_prob = pd.DataFrame(gb_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['GradientBoostingClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8141695702671312 fpr = 0.18337129840546698 threshold = 0.5201111213823948


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.52 = 81.48361


# gb_mod_all = GradientBoostingClassifier(n_estimators=100, learning_rate=0.05, max_depth=5, min_samples_leaf=3) 
# gb_mod_all.fit(X, y)

# print('Acc Train =', round(gb_mod_all.score(X, y) * 100, 2))

# y_pred_prob = pd.DataFrame(gb_mod_all.predict_proba(X)[:, 1])
# best_threshold = get_roc_curve (y, y_pred_prob)


pca_model = PCA()
pca_model.fit(X)
pca_model.explained_variance_

array([8.01253617e-01, 7.08186507e-01, 5.50042152e-01, 4.92000550e-01,
       4.18913543e-01, 2.88391322e-01, 2.11822823e-01, 1.95599705e-01,
       1.31432563e-01, 1.21141076e-01, 1.05553843e-01, 1.01045758e-01,
       9.06879306e-02, 6.78450093e-02, 5.48487343e-02, 4.73572724e-02,
       4.51527983e-02, 4.16213982e-02, 4.07905392e-02, 3.78172956e-02,
       3.68256941e-02, 3.44146900e-02, 2.15689056e-02, 1.77277718e-02,
       1.77043695e-02, 1.63565874e-02, 1.42079741e-02, 1.26515273e-02,
       1.13545330e-02, 8.96807853e-03, 5.73974941e-03, 4.39572055e-03,
       3.51034175e-03, 6.49402908e-04, 9.42942285e-14, 2.51186384e-14,
       1.56139309e-14, 7.41888969e-15, 4.78846487e-15, 1.73407730e-15,
       1.73407730e-15, 1.73407730e-15, 1.73407730e-15, 1.17523376e-15],
      dtype=float32)


plt.figure(figsize=(8,6))
plt.plot(np.cumsum(pca_model.explained_variance_ratio_))


plt.xlabel("PC Components")
plt.ylabel("Explained Variance")

Text(0, 0.5, 'Explained Variance')


pca_model_90 = PCA(n_components=2)
pca_model_90.fit(X)

X_train_auto_reduced = pca_model_90.transform(X)
X_train_auto_reduced.shape

(8693, 2)


plt.figure(figsize=(10,8))

plt.scatter(X_train_auto_reduced[:, 0], X_train_auto_reduced[:, 1], c=y)

plt.xlabel("Component 0")
plt.ylabel("Component 1")

Text(0, 0.5, 'Component 1')


# X_train_auto_reduced = pd.DataFrame(X_train_auto_reduced)

# X_pca = pd.concat((X, X_train_auto_reduced), axis=1)
# X_pca


# pca_model_90.fit(X_test)

# X_test_auto_reduced = pca_model_90.transform(X_test)
# X_test_auto_reduced.shape

# X_test_auto_reduced = pd.DataFrame(X_test_auto_reduced)

# X_test_pca = pd.concat((X_test, X_test_auto_reduced), axis=1)
# X_test_pca


# gb_with_pca_mod = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, max_depth=4, min_samples_leaf=3) 
# gb_with_pca_mod.fit(X_pca, y)

# print('Acc Train =', round(gb_with_pca_mod.score(X_pca, y) * 100, 2))
# print('Acc Test =', round(gb_with_pca_mod.score(X_test_pca, y_test) * 100, 2))


param_grid = {
    'C': [0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5],
    'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],
    'gamma': ['scale', 'auto']
}

SVC_gridsearch_hp_tuning = GridSearchCV(
        SVC(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 2
)

SVC_gridsearch_hp_tuning.fit(X_train, y_train)
print(SVC_gridsearch_hp_tuning.best_params_)

print("best_score = ", SVC_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 56 candidates, totalling 280 fits
{'C': 1.5, 'gamma': 'scale', 'kernel': 'poly'}
best_score =  0.7935010783608915


svc_mod = SVC(C = 1.5, gamma = 'scale', kernel = 'poly', probability = True)
svc_mod.fit(X_train, y_train)

print('Acc Train =', round(svc_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(svc_mod.score(X_test, y_test) * 100, 2))

model_acc['SVC'] = (round(svc_mod.score(X_test, y_test) * 100, 2), svc_mod)

Acc Train = 81.85
Acc Test = 79.64


y_test_pred_prob = pd.DataFrame(svc_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['SVC'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8385598141695703 fpr = 0.23462414578587698 threshold = 0.456507117667376


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.457 = 80.10351


param_grid = {
    'n_neighbors': [2, 3, 5, 8, 10, 12],
    'p': [1, 2]
}

kNeighborsClassifier_gridsearch_hp_tuning = GridSearchCV(
        KNeighborsClassifier(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 2
)

kNeighborsClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(kNeighborsClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", kNeighborsClassifier_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 12 candidates, totalling 60 fits
{'n_neighbors': 12, 'p': 2}
best_score =  0.7622956415600806


kn_mod = KNeighborsClassifier(n_neighbors=12, p=2)
kn_mod.fit(X_train, y_train)

print('Acc Train =', round(kn_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(kn_mod.score(X_test, y_test) * 100, 2))

model_acc['KNeighborsClassifier'] = (round(kn_mod.score(X_test, y_test) * 100, 2), kn_mod)

Acc Train = 80.21
Acc Test = 76.6


y_test_pred_prob = pd.DataFrame(kn_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['KNeighborsClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.6968641114982579 fpr = 0.1662870159453303 threshold = 0.5833333333333334


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.583 = 76.59574


param_grid = {
    "learning_rate": [0.01, 0.05, 0.1, 0.2, 0.5],
    "n_estimators": [50, 100, 150, 200, 250],
    'max_depth' : [3, 5, 8]
}


XGBClassifier_gridsearch_hp_tuning = GridSearchCV(
        XGBClassifier(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 2
)

XGBClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(XGBClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", XGBClassifier_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 75 candidates, totalling 375 fits
{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250}
best_score =  0.8065878799476595


xgb_mod = XGBClassifier(n_estimators=250, learning_rate=0.1, max_depth=3)
xgb_mod.fit(X_train, y_train)

print('Acc Train =', round(xgb_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(xgb_mod.score(X_test, y_test) * 100, 2))

model_acc['XGBClassifier'] = (round(xgb_mod.score(X_test, y_test) * 100, 2), xgb_mod)

Acc Train = 85.09
Acc Test = 80.56


y_test_pred_prob = pd.DataFrame(xgb_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['XGBClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8176538908246226 fpr = 0.1958997722095672 threshold = 0.51270777


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.513 = 81.02358


# xgb_mod = XGBClassifier(n_estimators=100, learning_rate=0.1, max_depth=5)
# xgb_mod.fit(X, y)

# print('Acc Train =', round(xgb_mod.score(X, y) * 100, 2))


param_grid = {
    "learning_rate": [0.01, 0.05, 0.1, 0.5],
    "n_estimators": [100, 200, 300, 400, 500],
    'max_depth' : [3, 5, 8]
}


catBoostClassifier_gridsearch_hp_tuning = GridSearchCV(
        CatBoostClassifier(),
        param_grid=param_grid,
        n_jobs=-1,
        verbose = 2
)

catBoostClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(catBoostClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", catBoostClassifier_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
0:	learn: 0.6715886	total: 147ms	remaining: 1m 13s
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{'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 500}
best_score =  0.8124825057279841


cbc_mod = CatBoostClassifier(n_estimators=500, learning_rate=0.05, max_depth=5)

cbc_mod.fit(X_train, y_train)

print('Acc Train =', round(cbc_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(cbc_mod.score(X_test, y_test) * 100, 2))

model_acc['CatBoostClassifier'] = (round(cbc_mod.score(X_test, y_test) * 100, 2), cbc_mod)

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Acc Train = 87.5
Acc Test = 82.17


y_test_pred_prob = pd.DataFrame(cbc_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['CatBoostClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8397212543554007 fpr = 0.1947608200455581 threshold = 0.4969305625069996


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.497 = 82.17366


cbc_mod_all = CatBoostClassifier(n_estimators=500, learning_rate=0.05, max_depth=5) #var1 BEST!!!!!

# cbc_mod_all = CatBoostClassifier(n_estimators=400, learning_rate=0.05, max_depth=5) #var1
# cbc_mod_all = CatBoostClassifier(n_estimators=100, learning_rate=0.01, max_depth=8) #var2 schlechter als var1
# {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 100}
cbc_mod_all.fit(X, y)

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<catboost.core.CatBoostClassifier at 0x1bc35c2cfa0>


y_pred_prob = pd.DataFrame(cbc_mod_all.predict_proba(X)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y, y_pred_prob)

print(best_threshold)

tpr = 0.8492462311557789 fpr = 0.11077636152954809 threshold = 0.5299397069432288
0.5299397069432288


param_grid = {
    "learning_rate": [0.01, 0.05, 0.1, 0.2, 0.5],
    "n_estimators": [50, 100, 150, 200, 250],
    'max_depth' : [3, 5, 8, 12]#,
 #   'min_samples_leaf' : [1, 3, 5, 8]
}


LGBMClassifier_gridsearch_hp_tuning = GridSearchCV(
        LGBMClassifier(),
        param_grid=param_grid,
      #  scoring="accuracy",
        n_jobs=-1,
        verbose = 2
)

LGBMClassifier_gridsearch_hp_tuning.fit(X_train, y_train)
print(LGBMClassifier_gridsearch_hp_tuning.best_params_)

print("best_score = ", LGBMClassifier_gridsearch_hp_tuning.best_score_)

Fitting 5 folds for each of 100 candidates, totalling 500 fits
{'learning_rate': 0.05, 'max_depth': 12, 'n_estimators': 150}
best_score =  0.8097503478166423


lgbmc_mod= LGBMClassifier(learning_rate = 0.05, max_depth = 12, n_estimators = 150)
lgbmc_mod.fit(X_train, y_train)

print('Acc Train =', round(lgbmc_mod.score(X_train, y_train) * 100, 2))
print('Acc Test =', round(lgbmc_mod.score(X_test, y_test) * 100, 2))

model_acc['LGBMClassifier'] = (round(lgbmc_mod.score(X_test, y_test) * 100, 2), lgbmc_mod)

Acc Train = 88.84
Acc Test = 81.08


y_test_pred_prob = pd.DataFrame(cbc_mod.predict_proba(X_test)[:, 1])

fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
roc_curves['LGBMClassifier'] = (fpr, tpr, best_threshold_ind)
plot_roc_curve(fpr, tpr, best_threshold_ind)

tpr = 0.8397212543554007 fpr = 0.1947608200455581 threshold = 0.4969305625069996


y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
y_test_pred
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

ACC with threshold 0.497 = 82.17366


lgbmc_mod_all = CatBoostClassifier(n_estimators=100, learning_rate=0.05, max_depth=12) #var1
#{'learning_rate': 0.05, 'max_depth': 12, 'n_estimators': 100}

lgbmc_mod_all.fit(X, y)

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<catboost.core.CatBoostClassifier at 0x1bc35c3cfd0>


for m in model_acc.keys():
    print(m, model_acc[m][0])

LogisticRegression 78.32
DecisionTreeClassifier 78.55
RandomForestClassifier 80.85
GradientBoostingClassifier 81.14
SVC 79.64
KNeighborsClassifier 76.6
XGBClassifier 80.56
CatBoostClassifier 82.17
LGBMClassifier 81.08


fig, axs = plt.subplots(figsize=(15, 8))
colors= []

plt.plot([0, 1], [0, 1], linestyle='dashed', color='red')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC curve')

for model in roc_curves.keys():
    plt.plot(roc_curves[model][0], roc_curves[model][1], label=model)
    axs.legend()


roc_curves['DecisionTreeClassifier'][0]

array([0.        , 0.        , 0.0022779 , 0.00341686, 0.00455581,
       0.00569476, 0.01252847, 0.02505695, 0.04214123, 0.05922551,
       0.06036446, 0.07630979, 0.09339408, 0.12186788, 0.1309795 ,
       0.14578588, 0.14920273, 0.18451025, 0.22209567, 0.26537585,
       0.26537585, 0.27790433, 0.29157175, 0.3405467 , 0.34624146,
       0.72665148, 0.77448747, 0.95216401, 0.97494305, 1.        ])


# LGBMClassifier                81.08
# GradientBoostingClassifier    81.14
# CatBoostClassifier            82.17


# xgb_mod = XGBClassifier(n_estimators=250, learning_rate=0.1, max_depth=3)
# xgb_mod.fit(X_train, y_train)

y_test_pred_gb_mod = gb_mod.predict_proba(X_test)[:, 1]
y_test_pred_cbc_mod = cbc_mod.predict_proba(X_test)[:, 1]
y_test_pred_lgbmc_mod = lgbmc_mod.predict_proba(X_test)[:, 1]

y_test_pred_prob = pd.DataFrame((y_test_pred_gb_mod + y_test_pred_cbc_mod + y_test_pred_lgbmc_mod) / 3)


fpr, tpr, best_threshold_ind, best_threshold = get_roc_curve(y_test, y_test_pred_prob)
y_test_pred = y_test_pred_prob.applymap(lambda x: 1 if x > best_threshold else 0)
print('ACC with threshold', round(best_threshold, 3), '=', round(accuracy_score(y_test, y_test_pred) *100, 5))

tpr = 0.8315911730545877 fpr = 0.19248291571753987 threshold = 0.5081899358189764
ACC with threshold 0.508 = 81.88614

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---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-199-c32dba8c5208> in <module>
      6 
      7 lgbmc_mod_all= LGBMClassifier(learning_rate = 0.05, max_depth = 12, n_estimators = 150)
----> 8 lgbmc_mod_all.fit(X)

TypeError: fit() missing 1 required positional argument: 'y'


validation_df = pd.read_csv("test.csv", sep=',', engine='python')
validation_df


passengerId_df = validation_df.loc[:, ['PassengerId']]

validation_df, am, em = data_transform(validation_df, for_visual_df, age_medians_all, expenses_means_all)

validation_df = encode_data(validation_df, nominal_encoder_all, scaler_all)

validation_df = memory_optimiz(validation_df)

validation_df

------ HomePlanet_update ------------
HomePlanet NULLs: 87
1. (after replacement through the GroupId) HomePlanet NULLs: 87
2. (after replacement through the Deck) HomePlanet NULLs: 51
3. (after replacement through the LastName) HomePlanet NULLs: 9
4. (after replacement through the Destination): 0
5. (after replacement by the most common value) HomePlanet NULLs: 0
------ Destination_update ------------
Destination NULLs: 92
(after replacement by the most common value) Destination NULLs: 0
------ LastName_update ------------
LastName NULLs: 94
(3012, 2)
(after update) LastName NULLs: 51
namesakes_num_in_group NULLs: 51
namesakes_num_in_group NULLs: 0
------ CabinDeck_update ------------
CabinDeck NULLs: 100
(after update throw GroupId) CabinDeck NULLs: 63
(after update throw HomePlanet) CabinDeck NULLs: 0
------ CabinNum_update ------------
CabinNum NULLs: 100
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'T']
A (1, 1)
B (11, 1)
C (6, 1)
D (1, 1)
E (2, 1)
F (25, 1)
G (54, 1)
CabinNum NULLs: 0
------ CabinSide_update ------------
CabinSide NULLs: 100
CabinSide NULLs: 100
CabinSide NULLs: 0
------ VIP_update ------------
VIP NULLs: 93
VIP NULLs: 0
------ CryoSleep_update ------------
93
57
0
------ Age_update ------------
Nulls in Expenses: 91
Nulls in Expenses: 0
Nulls in Expenses: 0
------ Expenses_update ------------
Nulls in Expenses: 467
Nulls in Expenses: 426
Nulls in Expenses: 275
Nulls in Expenses: 275
Nulls in Expenses: 0
------ log_expenses ------------
------ New_features_update ------------
------ bool_to_int ------------
------ memory_optimiz ------------
Reduction = 48.89%


gb_mod_all = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1, max_depth=3, min_samples_leaf=5) 
gb_mod_all.fit(X, y)

cbc_mod_all = CatBoostClassifier(n_estimators=500, learning_rate=0.05, max_depth=5)
cbc_mod_all.fit(X, y)

rf_mod_all = RandomForestClassifier(max_depth=10, min_samples_leaf=1, n_estimators = 250)
rf_mod_all.fit(X, y)

lgbmc_mod_all= LGBMClassifier(learning_rate = 0.05, max_depth = 12, n_estimators = 150)
lgbmc_mod_all.fit(X, y)

xgb_mod_all = XGBClassifier(n_estimators=250, learning_rate=0.1, max_depth=3)
xgb_mod_all.fit(X, y)



y_val_pred_gb_mod = gb_mod_all.predict_proba(validation_df)[:, 1]
y_val_pred_cbc_mod = cbc_mod_all.predict_proba(validation_df)[:, 1]
y_val_pred_lgbmc_mod = lgbmc_mod_all.predict_proba(validation_df)[:, 1]
y_val_pred_rf_mod = rf_mod_all.predict_proba(validation_df)[:, 1]
y_val_pred_xgb_mod = xgb_mod_all.predict_proba(validation_df)[:, 1]

y_val_pred_prob = pd.DataFrame((y_val_pred_gb_mod + y_val_pred_cbc_mod + y_val_pred_lgbmc_mod
                               + y_val_pred_rf_mod + y_val_pred_xgb_mod) / 5)

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# ------------------ y_val_pred_prob = pd.DataFrame(cbc_mod_all.predict_proba(validation_df)[:, 1])

#  y_val_pred = lgbmc_mod_all.predict(validation_df_pca)

# y_val_pred = lgbmc_mod_all.predict(validation_df)


#########y_val_pred = y_val_pred_prob.applymap(lambda x: x > 0.47419098173462404)

# y_val_pred = y_val_pred.astype(bool)
# y_val_pred


fig, axs = plt.subplots(figsize=(12, 6))
sns.histplot( x=y_val_pred_prob.loc[:, 0], bins=40, kde=True)

plt.xlabel('Predicted Propabilities')

Text(0.5, 0, 'Predicted Propabilities')


# der Prozentsatz der transportierten Passagiere im ursprünglichen Datensatz
transported_prc = y.value_counts(normalize=True)[1]
print('Transported % = ', transported_prc)

thresholds = np.linspace(0, 0.95, num=200, endpoint=False)

res = {}

for t in thresholds:
    
    tmp_y_val_pred = y_val_pred_prob.applymap(lambda x: x > t)
    res[t] = abs(tmp_y_val_pred.value_counts(normalize=True)[True] - transported_prc)
    
    
best_threshold = min(res, key=res.get)    
print('best_threshold =', best_threshold)

Transported % =  0.5036236051995858
best_threshold = 0.5177499999999999


y_val_pred = y_val_pred_prob.applymap(lambda x: x > best_threshold)


passengerId_df['Transported'] = y_val_pred
passengerId_df


passengerId_df.to_csv('sample_submission.csv', index=False)

	Columns	transported %	not_transported %
0	HomePlanet	51.24	48.76
1	CryoSleep	48.85	51.15
2	Cabin	50.25	49.75
3	Destination	50.55	49.45
4	Age	50.28	49.72
5	VIP	51.23	48.77
6	RoomService	45.86	54.14
7	FoodCourt	54.10	45.90
8	ShoppingMall	54.81	45.19
9	Spa	49.73	50.27
10	VRDeck	52.13	47.87
11	Name	50.50	49.50

	GroupId	HomePlanet_num
0	1.0	1
4068	6128.0	1
4077	6141.0	1
4076	6139.0	1
4075	6138.0	1
...	...	...
2033	3083.0	1
2032	3082.0	1
2031	3081.0	1
2030	3080.0	1
6106	9280.0	1

	IsSingle	VIP	HomePlanet	RoomService	FoodCourt	ShoppingMall	Spa	VRDeck
0	False	False	Earth	232.229602	219.576923	206.981025	245.934489	215.865275
1	False	False	Europa	302.703704	2587.694323	289.389868	1612.439560	1593.158242
2	False	False	Mars	1016.549153	106.812081	510.896907	196.180272	90.174497
3	False	True	Europa	438.547170	2905.092593	188.452830	1220.000000	2686.283019
4	False	True	Mars	874.750000	146.750000	657.210526	176.476190	44.000000
5	True	False	Earth	217.863158	217.633958	211.233844	220.979006	214.701048
6	True	False	Europa	203.042017	2618.831933	288.394958	1474.092697	1547.339833
7	True	False	Mars	990.223214	101.777778	583.811947	167.164080	91.442953
8	True	True	Europa	359.055556	2992.617647	223.971429	1439.722222	1835.472222
9	True	True	Mars	860.062500	108.516129	358.878788	231.303030	51.848485

	Age	RoomService	FoodCourt	ShoppingMall	Spa	VRDeck	GroupId	NumInGroup	CabinNum	TotalSpend	GroupSize	namesakes_num_in_group	NameLength
count	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000	6954.000000
mean	28.704630	0.792112	0.853224	0.726385	0.818248	0.783067	4628.054357	1.522577	599.634978	3.973037	1.838079	0.669255	13.583405
std	14.320889	1.200016	1.277488	1.129154	1.205654	1.200326	2662.087544	1.064101	508.683487	3.802163	1.373543	1.152245	3.057250
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000
25%	19.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2308.000000	1.000000	170.000000	0.000000	1.000000	0.000000	12.000000
50%	27.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4643.000000	1.000000	428.500000	4.446009	1.000000	0.000000	14.000000
75%	37.000000	1.819544	1.959041	1.518514	1.812913	1.724276	6864.000000	2.000000	998.000000	7.003803	2.000000	1.000000	16.000000
max	79.000000	3.996555	4.474420	4.370938	4.350422	4.382629	9280.000000	8.000000	1891.000000	17.251366	8.000000	6.000000	18.000000

Kaggle Competition Spaceship Titanic¶

Datenbeschreibung¶

Bibliotheken importieren¶

Data einlesen¶

Datenübersicht & NULL Werte¶

Data Visualisierung¶

Neue Features erzeugen¶

Verteilung der Zielvariable¶

Visualisierung von kategorialen Features¶

Visualisierung von numerischen Features¶

Data Vorbereitung¶

HomePlanet¶

Destination¶

LastName¶

CabinDeck¶

CabinNum¶

CabinSide¶

VIP¶

CryoSleep¶

Age¶

Expenses ("RoomService", "FoodCourt", "ShoppingMall", "Spa", 'VRDeck')¶

Speicherplatz Optimierung¶

Correlationsmatrix¶

Test Daten Vorbereitung¶

MLs¶

Logistic Regression¶

DecisionTreeClassifier¶

RandomForestClassifier¶

GradientBoostingClassifier¶

PCA¶

SVM¶

KNeighborsClassifier¶

XGBClassifier¶

CatBoostClassifier¶

LGBMClassifier¶

ROC PLOT¶

Modell-Ensemble¶

Final Forcast¶

	PassengerId	HomePlanet	CryoSleep	Cabin	Destination	Age	VIP	RoomService	FoodCourt	ShoppingMall	Spa	VRDeck	Name	Transported
0	0001_01	Europa	False	B/0/P	TRAPPIST-1e	39.0	False	0.0	0.0	0.0	0.0	0.0	Maham Ofracculy	False
1	0002_01	Earth	False	F/0/S	TRAPPIST-1e	24.0	False	109.0	9.0	25.0	549.0	44.0	Juanna Vines	True
2	0003_01	Europa	False	A/0/S	TRAPPIST-1e	58.0	True	43.0	3576.0	0.0	6715.0	49.0	Altark Susent	False
3	0003_02	Europa	False	A/0/S	TRAPPIST-1e	33.0	False	0.0	1283.0	371.0	3329.0	193.0	Solam Susent	False
4	0004_01	Earth	False	F/1/S	TRAPPIST-1e	16.0	False	303.0	70.0	151.0	565.0	2.0	Willy Santantines	True

	Column	Num Nulls	% Nulls
0	PassengerId	0	0.00
1	HomePlanet	201	2.31
2	CryoSleep	217	2.50
3	Cabin	199	2.29
4	Destination	182	2.09
5	Age	179	2.06
6	VIP	203	2.34
7	RoomService	181	2.08
8	FoodCourt	183	2.11
9	ShoppingMall	208	2.39
10	Spa	183	2.11
11	VRDeck	188	2.16
12	Name	200	2.30
13	Transported	0	0.00

	PassengerId	HomePlanet	CryoSleep	Cabin	Destination	Age	VIP	RoomService	FoodCourt	ShoppingMall	Spa	VRDeck	Name	Transported
1203	1284_01	Mars	True	F/247/S	NaN	NaN	False	0.0	NaN	0.0	0.0	0.0	Hal Knité	True
2639	2822_02	Earth	NaN	G/450/S	TRAPPIST-1e	5.0	NaN	0.0	0.0	0.0	0.0	NaN	Salley Harverez	False
2762	2970_01	Earth	False	NaN	TRAPPIST-1e	NaN	False	740.0	82.0	6.0	NaN	1.0	Dwin Adkinson	False
3072	3315_01	Earth	NaN	F/627/S	TRAPPIST-1e	15.0	False	10.0	0.0	99.0	NaN	2031.0	NaN	False
3535	3790_01	NaN	True	G/620/P	TRAPPIST-1e	13.0	False	0.0	NaN	0.0	NaN	0.0	Trick Meyersones	True
3882	4167_01	Earth	False	NaN	PSO J318.5-22	NaN	NaN	0.0	440.0	0.0	0.0	334.0	Ninaha Deckerson	False
4164	4446_05	Europa	NaN	B/175/S	TRAPPIST-1e	33.0	False	0.0	4017.0	NaN	NaN	2260.0	Phah Chocaters	True
4548	4840_01	NaN	True	F/915/S	TRAPPIST-1e	36.0	False	0.0	0.0	NaN	0.0	0.0	NaN	True
5208	5555_01	NaN	False	G/896/S	NaN	43.0	NaN	1.0	0.0	213.0	7.0	701.0	Winia Blanglison	True
5409	5777_01	Earth	NaN	F/1199/P	PSO J318.5-22	46.0	NaN	559.0	25.0	NaN	22.0	765.0	Katen River	False
5806	6141_02	Earth	False	NaN	NaN	21.0	False	28.0	0.0	0.0	662.0	0.0	NaN	False
6057	6405_02	Earth	NaN	NaN	NaN	2.0	False	0.0	0.0	0.0	0.0	0.0	Feline Toddleton	True
6112	6451_01	Mars	False	NaN	NaN	37.0	False	610.0	NaN	410.0	148.0	14.0	Carkes Panad	False
6904	7314_01	Europa	NaN	C/237/P	NaN	33.0	False	0.0	0.0	0.0	NaN	0.0	Aldun Venticable	True
7019	7472_01	Mars	True	F/1426/S	TRAPPIST-1e	NaN	False	0.0	0.0	0.0	0.0	NaN	NaN	True
7211	7703_02	NaN	True	G/1251/S	TRAPPIST-1e	13.0	False	0.0	0.0	NaN	0.0	0.0	NaN	True
7682	8202_03	NaN	True	C/306/S	TRAPPIST-1e	NaN	False	0.0	0.0	0.0	0.0	NaN	Charga Unkcatted	True

	GroupId	cabins
1388	2092.0	4.0
5459	8129.0	4.0
1132	1709.0	4.0
5814	8668.0	3.0
2611	3911.0	3.0
...	...	...
2155	3217.0	1.0
2153	3215.0	1.0
2152	3213.0	1.0
2151	3212.0	1.0
6216	9280.0	1.0

	PassengerId	HomePlanet	CryoSleep	Cabin	Destination	Age	VIP	RoomService	FoodCourt	ShoppingMall	...	NoSpend	IsChild	namesakes_num_in_group	NameLength	RoomService_log	FoodCourt_log	ShoppingMall_log	Spa_log	VRDeck_log	TotalSpend_log
291	0321_01	NaN	False	F/61/S	TRAPPIST-1e	59.0	True	1018.0	0.0	209.0	...	False	False	0.0	12.0	3.008174	0.000000	2.322219	0.000000	0.000000	3.089198
365	0402_01	NaN	True	D/15/S	55 Cancri e	32.0	True	0.0	0.0	0.0	...	True	False	0.0	12.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
405	0444_02	NaN	False	F/99/P	TRAPPIST-1e	26.0	True	1869.0	0.0	136.0	...	False	False	1.0	11.0	3.271842	0.000000	2.136721	0.000000	0.000000	3.302331
7042	7500_01	NaN	False	F/1432/S	TRAPPIST-1e	54.0	True	1770.0	7.0	228.0	...	False	False	0.0	11.0	3.248219	0.903090	2.359835	0.000000	0.000000	3.302331
7786	8314_02	NaN	False	D/245/S	55 Cancri e	29.0	True	0.0	2949.0	0.0	...	False	False	1.0	17.0	0.000000	3.469822	0.000000	0.477121	2.816241	3.557026

	PassengerId	HomePlanet	CryoSleep	Cabin	Destination	Age	VIP	RoomService	FoodCourt	ShoppingMall	...	LastName	GroupId	NumInGroup	CabinDeck	CabinNum	CabinSide	IsChild	TotalSpend	GroupSize	IsSingle
0	6309_02	Earth	NaN	G/1023/S	TRAPPIST-1e	7.0	False	0.0	0.0	0.0	...	Rodricker	6309.0	2.0	G	1023.0	S	True	0.0	4	False
1	2908_02	NaN	False	F/553/S	TRAPPIST-1e	13.0	False	649.0	2.0	0.0	...	Berreranks	2908.0	2.0	F	553.0	S	True	918.0	1	True
2	0548_01	Earth	False	E/36/S	TRAPPIST-1e	24.0	False	0.0	86.0	704.0	...	Webstes	548.0	1.0	E	36.0	S	False	791.0	1	True
3	8757_01	Earth	False	G/1409/S	TRAPPIST-1e	28.0	False	1.0	1484.0	210.0	...	Danielps	8757.0	1.0	G	1409.0	S	False	1700.0	1	True
4	1644_01	Earth	False	F/327/P	55 Cancri e	14.0	False	0.0	2.0	0.0	...	Waltonnedy	1644.0	1.0	F	327.0	P	True	796.0	1	True

	LastName	num_planets
0	Acobson	1
1475	Parrett	1
1469	Panie	1
1470	Panspic	1
1471	Parbage	1
...	...	...
731	Flynner	1
730	Flyncharlan	1
729	Floydendley	1
728	Flowensley	1
2208	Youngrayes	1

	LastName	num_planets	not_null_planet
1858	Slable	3	PSO J318.5-22
2099	Villenson	3	PSO J318.5-22
610	Dotsondez	3	55 Cancri e
2102	Vinozarks	3	TRAPPIST-1e
2103	Vinston	3	PSO J318.5-22
...	...	...	...
1018	Howence	1	PSO J318.5-22
1017	Howayery	1	TRAPPIST-1e
1015	Hotty	1	TRAPPIST-1e
1012	Horthy	1	TRAPPIST-1e
2208	Youngrayes	1	TRAPPIST-1e

	GroupId	num_planets	not_null_planet
4426	6672.0	3	TRAPPIST-1e
885	1350.0	3	55 Cancri e
5899	8956.0	3	TRAPPIST-1e
1916	2892.0	3	PSO J318.5-22
5860	8886.0	3	55 Cancri e
...	...	...	...
2477	3757.0	1	TRAPPIST-1e
2476	3756.0	1	TRAPPIST-1e
420	631.0	1	TRAPPIST-1e
2474	3754.0	1	TRAPPIST-1e
0	1.0	1	TRAPPIST-1e

	GroupId	not_null_last_name
0	1.0	Ofracculy
1	2.0	Vines
2	3.0	Susent
3	4.0	Santantines
4	5.0	Hinetthews
...	...	...
6108	9275.0	Conable
6109	9276.0	Noxnuther
6110	9278.0	Mondalley
6111	9279.0	Connon
6112	9280.0	Hontichre

	null_num	size
0	0	6606
1	1	1867
2	2	203
3	3	17

	max_spend	min_spend	count	min_age	max_age
CryoSleep
False	35987.0	0.0	5439	0.0	79.0
True	0.0	0.0	3037	0.0	78.0

	PassengerId	Transported
0	0013_01	True
1	0018_01	False
2	0019_01	True
3	0021_01	True
4	0023_01	True
...	...	...
4272	9266_02	True
4273	9269_01	False
4274	9271_01	True
4275	9273_01	True
4276	9277_01	True

	x0_Earth	x0_Europa	x0_Mars	x1_55 Cancri e	x1_PSO J318.5-22	x1_TRAPPIST-1e	x2_O	x2_P	x2_S	x3_A	...	x3_T	x4_Earth - 55 Cancri e	x4_Earth - PSO J318.5-22	x4_Earth - TRAPPIST-1e	x4_Europa - 55 Cancri e	x4_Europa - PSO J318.5-22	x4_Europa - TRAPPIST-1e	x4_Mars - 55 Cancri e	x4_Mars - PSO J318.5-22	x4_Mars - TRAPPIST-1e
0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
2	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
3	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
4	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6949	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6950	1.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
6951	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
6952	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
6953	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0

	x0_Earth	x0_Europa	x0_Mars	x1_55 Cancri e	x1_PSO J318.5-22	x1_TRAPPIST-1e	x2_O	x2_P	x2_S	x3_A	...	GroupId	NumInGroup	CabinNum	IsChild	TotalSpend	GroupSize	IsSingle	namesakes_num_in_group	NameLength	NoSpend
0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.679815	0.142857	0.540984	1.0	0.000000	0.428571	0.0	0.333333	0.833333	1.0
1	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.313288	0.142857	0.292438	1.0	0.437374	0.000000	1.0	0.000000	0.888889	0.0
2	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.058950	0.000000	0.019038	0.0	0.294976	0.000000	1.0	0.000000	0.777778	0.0
3	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.943636	0.000000	0.745108	0.0	0.381140	0.000000	1.0	0.000000	0.722222	0.0
4	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.177066	0.000000	0.172924	1.0	0.195781	0.000000	1.0	0.000000	0.888889	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6949	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.155620	0.000000	0.116869	1.0	0.000000	0.000000	1.0	0.000000	0.833333	1.0
6950	1.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	...	0.106261	0.000000	0.107879	1.0	0.406790	0.000000	1.0	0.000000	0.777778	0.0
6951	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.494773	0.285714	0.455843	0.0	0.000000	0.428571	0.0	0.500000	0.611111	1.0
6952	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.650393	0.142857	0.118985	1.0	0.497925	0.285714	0.0	0.333333	0.888889	0.0
6953	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.061429	0.000000	0.011105	0.0	0.000000	0.571429	0.0	0.666667	0.777778	1.0

	Features	importance
38	TotalSpend	13.423982
43	NoSpend	8.889983
30	FoodCourt	7.665911
32	Spa	7.534157
26	CryoSleep	7.199363
29	RoomService	7.194973
33	VRDeck	6.907424
31	ShoppingMall	5.928310
36	CabinNum	4.457665
34	GroupId	4.252145
27	Age	3.387283
0	x0_Earth	2.278378
42	NameLength	2.105918
1	x0_Europa	1.859197
13	x3_E	1.531294
19	x4_Earth - TRAPPIST-1e	1.347522
15	x3_G	1.231052
14	x3_F	1.170829
7	x2_P	1.124561
8	x2_S	1.124489
39	GroupSize	1.011184
41	namesakes_num_in_group	0.966701
35	NumInGroup	0.952475
2	x0_Mars	0.678656
25	x4_Mars - TRAPPIST-1e	0.596319
37	IsChild	0.548211
11	x3_C	0.532934
20	x4_Europa - 55 Cancri e	0.508754
10	x3_B	0.480592
22	x4_Europa - TRAPPIST-1e	0.450021
5	x1_TRAPPIST-1e	0.445491
40	IsSingle	0.379511
3	x1_55 Cancri e	0.337420
4	x1_PSO J318.5-22	0.322843
18	x4_Earth - PSO J318.5-22	0.242510
17	x4_Earth - 55 Cancri e	0.213325
12	x3_D	0.213291
9	x3_A	0.136160
28	VIP	0.125256
6	x2_O	0.085157
23	x4_Mars - 55 Cancri e	0.078498
24	x4_Mars - PSO J318.5-22	0.066421
21	x4_Europa - PSO J318.5-22	0.011114
16	x3_T	0.002720

	x0_Earth	x0_Europa	x0_Mars	x1_55 Cancri e	x1_PSO J318.5-22	x1_TRAPPIST-1e	x2_O	x2_P	x2_S	x3_A	...	GroupId	NumInGroup	CabinNum	IsChild	TotalSpend	GroupSize	IsSingle	namesakes_num_in_group	NameLength	NoSpend
0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.001293	0.000000	0.003082	0.0	0.000000	0.000000	1.0	0.000000	0.833333	1.0
1	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.001832	0.000000	0.003610	0.0	0.258001	0.000000	1.0	0.000000	0.777778	0.0
2	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.001940	0.000000	0.001501	0.0	0.000000	0.000000	1.0	0.000000	0.833333	1.0
3	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.002155	0.000000	0.002028	0.0	0.513060	0.000000	1.0	0.000000	0.888889	0.0
4	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.002371	0.000000	0.004137	0.0	0.222872	0.000000	1.0	0.000000	0.833333	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4272	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.998491	0.142857	0.790178	0.0	0.000000	0.142857	0.0	0.142857	0.611111	1.0
4273	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	...	0.998815	0.000000	0.793238	0.0	0.428165	0.000000	1.0	0.000000	0.722222	0.0
4274	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.999030	0.000000	0.157549	0.0	0.000000	0.000000	1.0	0.000000	0.611111	1.0
4275	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	...	0.999246	0.000000	0.158076	0.0	0.356356	0.000000	1.0	0.000000	0.777778	0.0
4276	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.999677	0.000000	0.791232	0.0	0.000000	0.000000	1.0	0.000000	0.888889	1.0